Persistent network model diagnostics - unbalanced statistics

This file shows diagnostics for persistent network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.

Load packages and model fits

rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")
## Loading required package: lattice
## Loading required package: RColorBrewer
library("knitr")
library("kableExtra")
library("here")
## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMP
load(file = here("Model fits and simulations/Fit tests and debugging/est/fit.p.buildup.unbal.rda"))

Model terms and control settings

Model terms and target statistics
Terms Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
edges 2018 2018.0 2018.0 2018.0 2018.0 2018.0 2018.0 2018.0
nodefactor.deg.main.1 NA NA NA 1684.0 1684.0 1684.0 1684.0 1684.0
nodefactor.race..wa.B NA 251.2 251.2 251.2 251.2 251.2 251.2 251.2
nodefactor.race..wa.H NA 388.9 388.9 388.9 388.9 388.9 388.9 388.9
nodefactor.region.EW NA NA NA NA 367.7 367.7 367.7 367.7
nodefactor.region.OW NA NA NA NA 1182.5 1182.5 1182.5 1182.5
concurrent NA NA NA NA NA NA 1385.0 1385.0
nodematch.race..wa.B NA NA 8.5 8.5 8.5 8.5 8.5 8.5
nodematch.race..wa.H NA NA 51.2 51.2 51.2 51.2 51.2 51.2
nodematch.race..wa.O NA NA 1246.8 1246.8 1246.8 1246.8 1246.8 1246.8
nodematch.region NA NA NA NA NA NA NA 1614.4
absdiff.sqrt.age NA NA NA NA NA 1665.3 1665.3 1665.3
degrange 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
nodematch.role.class.I -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf
nodematch.role.class.R -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf

The control settings for these models are:

set.control.ergm = ccontrol.ergm(MCMC.interval = 1e+5,
                                 MCMC.samplesize = 7500,
                                 MCMC.burnin = 1e+6,
                                 MPLE.max.dyad.types = 1e+7,
                                 init.method = "zeros",
                                 MCMLE.maxit = 400,
                                 parallel = np/2,
                                 parallel.type="PSOCK"))

MCMC diagnostics

Model 1

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##           Mean             SD       Naive SE Time-series SE 
##        -0.2186        40.1536         0.2318         0.2312 
## 
## 2. Quantiles for each variable:
## 
##  2.5%   25%   50%   75% 97.5% 
##   -79   -27     0    27    79 
## 
## 
## Sample statistics cross-correlations:
##       edges
## edges     1
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.013487554
## Lag 2e+05 -0.035782824
## Lag 3e+05 -0.020532232
## Lag 4e+05  0.007817567
## Lag 5e+05  0.003661391
## Chain 2 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.001671115
## Lag 2e+05 -0.022463497
## Lag 3e+05 -0.012324236
## Lag 4e+05 -0.010816892
## Lag 5e+05 -0.008168326
## Chain 3 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.008966853
## Lag 2e+05  0.005485714
## Lag 3e+05 -0.045421713
## Lag 4e+05 -0.006897222
## Lag 5e+05  0.031279296
## Chain 4 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.005000925
## Lag 2e+05  0.008226031
## Lag 3e+05  0.003451454
## Lag 4e+05  0.011273038
## Lag 5e+05 -0.028053163
## Chain 5 
##                   edges
## Lag 0      1.0000000000
## Lag 1e+05  0.0020077954
## Lag 2e+05 -0.0103842854
## Lag 3e+05 -0.0003697404
## Lag 4e+05  0.0233826165
## Lag 5e+05  0.0177610448
## Chain 6 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.023611076
## Lag 2e+05  0.002694384
## Lag 3e+05  0.005469468
## Lag 4e+05  0.002845718
## Lag 5e+05 -0.001076281
## Chain 7 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.006678144
## Lag 2e+05  0.015121069
## Lag 3e+05 -0.004985953
## Lag 4e+05 -0.003576797
## Lag 5e+05 -0.017363692
## Chain 8 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.011481261
## Lag 2e+05  0.017588090
## Lag 3e+05  0.004523673
## Lag 4e+05 -0.052315339
## Lag 5e+05 -0.009571548
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.3126 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.7546014 
## Joint P-value (lower = worse):  0.7487839 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## -1.36 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.1737631 
## Joint P-value (lower = worse):  0.188632 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.114 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2653338 
## Joint P-value (lower = worse):  0.262925 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.7309 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.4648672 
## Joint P-value (lower = worse):  0.4673233 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.7366 
## 
## Individual P-values (lower = worse):
##    edges 
## 0.461344 
## Joint P-value (lower = worse):  0.4586529 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.6506 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.5152947 
## Joint P-value (lower = worse):  0.5125573 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## -1.152 
## 
## Individual P-values (lower = worse):
##    edges 
## 0.249164 
## Joint P-value (lower = worse):  0.2434573 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## 0.06897 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.9450113 
## Joint P-value (lower = worse):  0.9432202 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 2

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean    SD Naive SE Time-series SE
## edges                  0.3733 40.35  0.23296        0.23294
## nodefactor.race..wa.B  0.5469 15.32  0.08846        0.09042
## nodefactor.race..wa.H -0.0133 19.59  0.11311        0.11268
## 
## 2. Quantiles for each variable:
## 
##                         2.5%    25%    50%   75% 97.5%
## edges                 -78.00 -27.00 0.0000 28.00 79.00
## nodefactor.race..wa.B -29.16 -10.16 0.8352 10.84 30.84
## nodefactor.race..wa.H -37.91 -12.91 0.0920 13.09 39.09
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000            0.32050628
## nodefactor.race..wa.B 0.3205063            1.00000000
## nodefactor.race..wa.H 0.4054482            0.04918763
##                       nodefactor.race..wa.H
## edges                            0.40544823
## nodefactor.race..wa.B            0.04918763
## nodefactor.race..wa.H            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.006554221          -0.006717729          -0.009683622
## Lag 2e+05  0.011957280          -0.029559718           0.010268905
## Lag 3e+05  0.004139475          -0.019093137           0.001464487
## Lag 4e+05 -0.004938479           0.003280830          -0.010539335
## Lag 5e+05 -0.024362461           0.001665306          -0.013782424
## Chain 2 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05  0.01194897           0.026728225           0.011213785
## Lag 2e+05 -0.01997238          -0.035150735           0.009879885
## Lag 3e+05 -0.03554968           0.003859359          -0.007344619
## Lag 4e+05 -0.02084855           0.022976757          -0.004207365
## Lag 5e+05  0.02966213          -0.014637060          -0.003341877
## Chain 3 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.032117295          -0.016584543          0.0230555989
## Lag 2e+05  0.007444965           0.017934058         -0.0020167452
## Lag 3e+05  0.008080309          -0.016135064         -0.0171629171
## Lag 4e+05 -0.013252445           0.004144110          0.0011410827
## Lag 5e+05 -0.030043649           0.001505045          0.0009293615
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.016147357           0.019427602          -0.003433325
## Lag 2e+05  0.003957918          -0.006910936          -0.002167345
## Lag 3e+05  0.021268152           0.024385735          -0.010024469
## Lag 4e+05 -0.016129658          -0.030616004           0.012822134
## Lag 5e+05  0.010888433          -0.009133953          -0.004842446
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.008645006           0.011780900           0.004060910
## Lag 2e+05  0.004953322           0.005421545           0.002295745
## Lag 3e+05 -0.020208704           0.016646870          -0.010157812
## Lag 4e+05 -0.001419087           0.002286020          -0.002614842
## Lag 5e+05 -0.027391459          -0.004234575          -0.022352739
## Chain 6 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000          1.0000000000           1.000000000
## Lag 1e+05  0.01016942          0.0544214362          -0.007005882
## Lag 2e+05  0.02545053         -0.0005276596          -0.011545210
## Lag 3e+05 -0.01960948          0.0262297090           0.008377690
## Lag 4e+05 -0.00640942          0.0083933890          -0.008986206
## Lag 5e+05 -0.01410669          0.0069180447           0.018535807
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.004571201           0.014024757         -0.0184098938
## Lag 2e+05  0.014593576          -0.025626125          0.0049212093
## Lag 3e+05 -0.021765919          -0.009416186         -0.0006888469
## Lag 4e+05 -0.026793779           0.009643306          0.0124393078
## Lag 5e+05 -0.024177115           0.031002345          0.0131789891
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.018270499          0.0113923592         -0.0308584005
## Lag 2e+05 -0.016149433          0.0030091822         -0.0016766501
## Lag 3e+05 -0.024231745          0.0005475025         -0.0282665482
## Lag 4e+05 -0.016740363          0.0003332694         -0.0093512908
## Lag 5e+05 -0.008473009          0.0199931748          0.0005746285
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              1.649138              1.340152             -0.004618 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.09911944            0.18019584            0.99631499 
## Joint P-value (lower = worse):  0.2780115 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.3241                0.2388                0.3444 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7458688             0.8112580             0.7305266 
## Joint P-value (lower = worse):  0.9055359 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                 1.202                 0.483                 1.137 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.2292330             0.6291053             0.2557139 
## Joint P-value (lower = worse):  0.5386789 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.9256               -0.5185               -0.5661 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3546760             0.6041435             0.5713258 
## Joint P-value (lower = worse):  0.8017229 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.0189                0.3985               -0.3357 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3082600             0.6902818             0.7371271 
## Joint P-value (lower = worse):  0.6614073 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.03285              -0.67817              -0.26158 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9737936             0.4976652             0.7936429 
## Joint P-value (lower = worse):  0.8904737 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.79895               1.14838              -0.05557 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4243211             0.2508114             0.9556824 
## Joint P-value (lower = worse):  0.4523851 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.62318               0.83140              -0.04352 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.5331635             0.4057493             0.9652908 
## Joint P-value (lower = worse):  0.8193655 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 3

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                          Mean     SD Naive SE Time-series SE
## edges                 -35.215 40.247  0.23237        0.23592
## nodefactor.race..wa.B  36.843 15.748  0.09092        0.09087
## nodefactor.race..wa.H  36.621 20.095  0.11602        0.11078
## nodematch.race..wa.B   -8.477  0.000  0.00000        0.00000
## nodematch.race..wa.H  -36.412  3.798  0.02193        0.02198
## nodematch.race..wa.O   37.193 33.131  0.19128        0.19331
## 
## 2. Quantiles for each variable:
## 
##                           2.5%     25%     50%     75%   97.5%
## edges                 -114.000 -62.000 -35.000  -8.000  44.000
## nodefactor.race..wa.B    6.835  25.835  36.835  47.835  67.835
## nodefactor.race..wa.H   -2.908  23.092  36.092  50.092  76.092
## nodematch.race..wa.B    -8.477  -8.477  -8.477  -8.477  -8.477
## nodematch.race..wa.H   -43.200 -39.200 -36.200 -34.200 -28.200
## nodematch.race..wa.O   -27.844  15.156  37.156  59.156 102.156
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                            edges nodefactor.race..wa.B
## edges                 1.00000000           0.352221542
## nodefactor.race..wa.B 0.35222154           1.000000000
## nodefactor.race..wa.H 0.44005669          -0.006100363
## nodematch.race..wa.B          NA                    NA
## nodematch.race..wa.H  0.08924169          -0.001999436
## nodematch.race..wa.O  0.79069122          -0.043970280
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                           0.440056693                   NA
## nodefactor.race..wa.B          -0.006100363                   NA
## nodefactor.race..wa.H           1.000000000                   NA
## nodematch.race..wa.B                     NA                    1
## nodematch.race..wa.H            0.352339220                   NA
## nodematch.race..wa.O           -0.028680803                   NA
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                          0.089241688           0.79069122
## nodefactor.race..wa.B         -0.001999436          -0.04397028
## nodefactor.race..wa.H          0.352339220          -0.02868080
## nodematch.race..wa.B                    NA                   NA
## nodematch.race..wa.H           1.000000000           0.01027457
## nodematch.race..wa.O           0.010274566           1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0025395876          -0.007050145          -0.022349892
## Lag 2e+05  0.0002144022           0.017917582          -0.015166343
## Lag 3e+05 -0.0141075319          -0.041900065          -0.009515247
## Lag 4e+05  0.0021146721           0.006754025          -0.020528122
## Lag 5e+05  0.0062868976           0.012217880           0.012964218
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.016832724         -0.002042829
## Lag 2e+05                  NaN         -0.012535316          0.004680582
## Lag 3e+05                  NaN         -0.015206862         -0.013417354
## Lag 4e+05                  NaN          0.006336900          0.011134573
## Lag 5e+05                  NaN         -0.003048031          0.005799247
## Chain 2 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000e+00           1.000000000           1.000000000
## Lag 1e+05 -4.779389e-03          -0.012476301          -0.029186951
## Lag 2e+05 -1.486821e-02          -0.005992004          -0.014901114
## Lag 3e+05  3.488001e-02          -0.013594430          -0.014482120
## Lag 4e+05 -1.498385e-02           0.010714475          -0.002572354
## Lag 5e+05  6.006984e-05          -0.020866890           0.002347319
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.022882124          0.005282790
## Lag 2e+05                  NaN          0.004187335         -0.026090402
## Lag 3e+05                  NaN          0.025926740          0.039893801
## Lag 4e+05                  NaN          0.011832242         -0.004409353
## Lag 5e+05                  NaN         -0.001448160         -0.006174433
## Chain 3 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.0194752793          -0.013390370          0.0220747724
## Lag 2e+05  0.0118727086           0.003035374          0.0008845601
## Lag 3e+05 -0.0016427349           0.021751905         -0.0037049907
## Lag 4e+05 -0.0005382807          -0.022894406          0.0270578585
## Lag 5e+05  0.0039570083          -0.005416584          0.0306995479
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.017669199         -0.028603654
## Lag 2e+05                  NaN         -0.004605939          0.016475963
## Lag 3e+05                  NaN          0.046496285         -0.010612591
## Lag 4e+05                  NaN         -0.006054243          0.000485405
## Lag 5e+05                  NaN          0.010791942         -0.013620821
## Chain 4 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.003167782           0.024710306          -0.032348920
## Lag 2e+05  0.010893139           0.007247245          -0.007991944
## Lag 3e+05 -0.001897216          -0.007249646          -0.030883762
## Lag 4e+05 -0.026879752          -0.006030720           0.007910120
## Lag 5e+05  0.006125311          -0.022027747           0.003457858
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.043936384          0.004604176
## Lag 2e+05                  NaN          0.005954585         -0.003465823
## Lag 3e+05                  NaN          0.012116516          0.010029710
## Lag 4e+05                  NaN          0.020583793         -0.018629889
## Lag 5e+05                  NaN         -0.002316752         -0.006001492
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.024531164           0.002215175          -0.026526431
## Lag 2e+05  0.015942909           0.021366319           0.014425610
## Lag 3e+05 -0.025675818           0.001476705           0.018609235
## Lag 4e+05 -0.008322712           0.011998448           0.005553276
## Lag 5e+05  0.004232813           0.031509877           0.013265472
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.027817322         -0.007594972
## Lag 2e+05                  NaN         -0.015737697          0.010442853
## Lag 3e+05                  NaN          0.007922660         -0.012306620
## Lag 4e+05                  NaN          0.004111782          0.009738144
## Lag 5e+05                  NaN          0.019929459         -0.003090154
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.016109397           0.006486776          -0.002069206
## Lag 2e+05  0.022594584           0.017598106          -0.002579903
## Lag 3e+05 -0.014343982          -0.010105222           0.005826830
## Lag 4e+05  0.007106531           0.004821466          -0.016874441
## Lag 5e+05 -0.016346025          -0.007857468          -0.015946328
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.000000e+00           1.00000000
## Lag 1e+05                  NaN         3.685830e-03           0.02369092
## Lag 2e+05                  NaN         3.340193e-03           0.02931499
## Lag 3e+05                  NaN         1.111162e-02          -0.01851355
## Lag 4e+05                  NaN        -1.972230e-02          -0.01131314
## Lag 5e+05                  NaN        -8.716786e-05          -0.03142971
## Chain 7 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.010846368          0.0086911478         -0.0145840872
## Lag 2e+05 -0.011821852         -0.0204958427         -0.0140439193
## Lag 3e+05 -0.001132228         -0.0116615284         -0.0003579546
## Lag 4e+05  0.019854873         -0.0354166418          0.0019165291
## Lag 5e+05 -0.022603939          0.0005646085          0.0210214525
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.024081035         -0.028997402
## Lag 2e+05                  NaN         -0.008079222          0.002907015
## Lag 3e+05                  NaN          0.006595599          0.028475684
## Lag 4e+05                  NaN         -0.031226018          0.007305972
## Lag 5e+05                  NaN         -0.009933457         -0.011224779
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.006717060          -0.009389680          -0.029230252
## Lag 2e+05  0.013051826          -0.004549981           0.007857429
## Lag 3e+05 -0.007143657           0.016169896           0.011255313
## Lag 4e+05 -0.014246527          -0.012711134           0.011844742
## Lag 5e+05  0.007510439           0.006241833           0.011368162
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.009371426          0.011945128
## Lag 2e+05                  NaN         -0.012900385         -0.020747355
## Lag 3e+05                  NaN         -0.007173687         -0.019166803
## Lag 4e+05                  NaN          0.024673026         -0.008660007
## Lag 5e+05                  NaN         -0.017606355          0.006663871
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -2.0167               -1.7950                0.5743 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.6437               -1.7737 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.04373203            0.07265336            0.56579209 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.51977201            0.07611146 
## Joint P-value (lower = worse):  0.1515322 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.08428              -1.70156              -0.25364 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               1.26489               0.86057 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.93283063            0.08883818            0.79977014 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.20590910            0.38947232 
## Joint P-value (lower = worse):  0.4999851 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.2064                0.7763               -0.5521 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.8283               -1.3337 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.2276747             0.4375972             0.5808545 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.4075050             0.1823100 
## Joint P-value (lower = worse):  0.5696944 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.08162               1.79350               0.97163 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              -0.67882              -1.40428 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.93495210            0.07289275            0.33123643 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.49725087            0.16023639 
## Joint P-value (lower = worse):  0.221311 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              0.001244              1.107957              1.316137 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              0.396876             -1.349703 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9990075             0.2678804             0.1881280 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.6914592             0.1771113 
## Joint P-value (lower = worse):  0.4748407 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.8935               -0.4100                2.1198 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.9054                1.1768 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.05829656            0.68180665            0.03402018 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.36524729            0.23926710 
## Joint P-value (lower = worse):  0.2899855 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.5554                0.1886               -1.7236 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                1.6638                0.4090 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.57860000            0.85042651            0.08478907 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.09615893            0.68257155 
## Joint P-value (lower = worse):  0.1043284 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.5244               -0.1275                2.0191 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               -0.4178               -0.2432 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##            0.60003410            0.89855080            0.04347235 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.67606519            0.80782372 
## Joint P-value (lower = worse):  0.7020861 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 4

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -35.1802 40.257  0.23243        0.23264
## nodefactor.deg.main.1   0.5313 45.530  0.26287        0.26195
## nodefactor.race..wa.B  38.5535 15.896  0.09178        0.09031
## nodefactor.race..wa.H  35.8103 20.048  0.11574        0.11512
## nodematch.race..wa.B   -8.4768  0.000  0.00000        0.00000
## nodematch.race..wa.H  -36.2771  3.813  0.02201        0.02157
## nodematch.race..wa.O   36.4615 33.179  0.19156        0.19092
## 
## 2. Quantiles for each variable:
## 
##                           2.5%     25%     50%     75%   97.5%
## edges                 -114.000 -62.000 -36.000  -8.000  44.000
## nodefactor.deg.main.1  -89.000 -30.000   1.000  31.000  89.000
## nodefactor.race..wa.B    7.835  27.835  38.835  48.835  69.835
## nodefactor.race..wa.H   -2.908  22.092  36.092  49.092  76.092
## nodematch.race..wa.B    -8.477  -8.477  -8.477  -8.477  -8.477
## nodematch.race..wa.H   -43.200 -39.200 -36.200 -34.200 -28.200
## nodematch.race..wa.O   -27.844  14.156  36.156  59.156 102.156
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.76203367
## nodefactor.deg.main.1 0.76203367            1.00000000
## nodefactor.race..wa.B 0.36268533            0.24190973
## nodefactor.race..wa.H 0.42261409            0.34448074
## nodematch.race..wa.B          NA                    NA
## nodematch.race..wa.H  0.07074788            0.06046846
## nodematch.race..wa.O  0.79234909            0.60750886
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.36268533            0.42261409
## nodefactor.deg.main.1            0.24190973            0.34448074
## nodefactor.race..wa.B            1.00000000           -0.02041070
## nodefactor.race..wa.H           -0.02041070            1.00000000
## nodematch.race..wa.B                     NA                    NA
## nodematch.race..wa.H            -0.01097115            0.34431529
## nodematch.race..wa.O            -0.02797774           -0.04210483
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                                   NA           0.07074788
## nodefactor.deg.main.1                   NA           0.06046846
## nodefactor.race..wa.B                   NA          -0.01097115
## nodefactor.race..wa.H                   NA           0.34431529
## nodematch.race..wa.B                     1                   NA
## nodematch.race..wa.H                    NA           1.00000000
## nodematch.race..wa.O                    NA          -0.00203172
##                       nodematch.race..wa.O
## edges                           0.79234909
## nodefactor.deg.main.1           0.60750886
## nodefactor.race..wa.B          -0.02797774
## nodefactor.race..wa.H          -0.04210483
## nodematch.race..wa.B                    NA
## nodematch.race..wa.H           -0.00203172
## nodematch.race..wa.O            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.001942744          -0.001140796          -0.011393414
## Lag 2e+05 -0.018540770          -0.010563570          -0.026676522
## Lag 3e+05 -0.001132814           0.004415958           0.021612902
## Lag 4e+05  0.006140194          -0.026929134           0.017553501
## Lag 5e+05 -0.007257402          -0.001448532           0.002410582
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05          -0.002987320                  NaN          0.006579022
## Lag 2e+05          -0.001870620                  NaN          0.016174841
## Lag 3e+05          -0.009269763                  NaN         -0.019716615
## Lag 4e+05           0.006419384                  NaN          0.012717694
## Lag 5e+05          -0.009884298                  NaN         -0.020071700
##           nodematch.race..wa.O
## Lag 0             1.0000000000
## Lag 1e+05         0.0043658782
## Lag 2e+05        -0.0173723391
## Lag 3e+05         0.0059636003
## Lag 4e+05         0.0003111173
## Lag 5e+05        -0.0138527797
## Chain 2 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.018240384          -0.010731653          -0.017962250
## Lag 2e+05  0.001174571           0.002318661           0.013715761
## Lag 3e+05 -0.019734937           0.006713202           0.002375300
## Lag 4e+05  0.030408187           0.011819220           0.002308762
## Lag 5e+05 -0.011850388          -0.001770853          -0.019002206
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.00000000                  NaN          1.000000000
## Lag 1e+05           -0.02469961                  NaN         -0.011580188
## Lag 2e+05           -0.01381426                  NaN         -0.013826362
## Lag 3e+05           -0.04011451                  NaN          0.008392337
## Lag 4e+05            0.01848514                  NaN         -0.026260148
## Lag 5e+05            0.01357708                  NaN         -0.000684475
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.015211484
## Lag 2e+05         -0.013110202
## Lag 3e+05         -0.005779592
## Lag 4e+05          0.008956427
## Lag 5e+05          0.004704919
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.004461168          -0.005433140          0.0082977157
## Lag 2e+05  0.004522178          -0.011630567          0.0002460714
## Lag 3e+05  0.019502342           0.009312217          0.0009693014
## Lag 4e+05  0.013516937           0.042228828         -0.0094089762
## Lag 5e+05 -0.029633441          -0.049651177         -0.0270108561
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000e+00                  NaN          1.000000000
## Lag 1e+05         -7.509683e-05                  NaN          0.005441897
## Lag 2e+05         -1.306972e-02                  NaN         -0.007885470
## Lag 3e+05          4.213026e-02                  NaN         -0.001924692
## Lag 4e+05         -2.425348e-02                  NaN          0.003809971
## Lag 5e+05          4.893917e-03                  NaN          0.013341748
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.023693833
## Lag 2e+05          0.017189155
## Lag 3e+05          0.006437583
## Lag 4e+05          0.031118125
## Lag 5e+05         -0.008659570
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.015917421          -0.013822103          -0.020135401
## Lag 2e+05 -0.017976882          -0.014398624          -0.013902637
## Lag 3e+05 -0.003268163           0.001966200          -0.008473962
## Lag 4e+05 -0.010668739          -0.009734738          -0.005538536
## Lag 5e+05 -0.001632559          -0.001151857           0.026177361
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN         1.0000000000
## Lag 1e+05          -0.023727363                  NaN        -0.0512159131
## Lag 2e+05          -0.002460876                  NaN         0.0128604321
## Lag 3e+05           0.020946351                  NaN        -0.0006981956
## Lag 4e+05          -0.019228003                  NaN        -0.0010939300
## Lag 5e+05          -0.021952908                  NaN        -0.0166150379
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.004657759
## Lag 2e+05         -0.004787134
## Lag 3e+05          0.007837227
## Lag 4e+05         -0.013544327
## Lag 5e+05         -0.006901445
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.024946867          -0.010931328           0.008848543
## Lag 2e+05  0.012760437           0.005431661           0.009138981
## Lag 3e+05  0.011060827           0.023530683           0.018187254
## Lag 4e+05  0.014898162          -0.008795485           0.002548699
## Lag 5e+05 -0.007714017          -0.007163429          -0.010384990
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN         1.000000e+00
## Lag 1e+05           0.018876804                  NaN        -9.344338e-05
## Lag 2e+05           0.001652251                  NaN         9.308547e-03
## Lag 3e+05           0.008876304                  NaN        -1.610398e-02
## Lag 4e+05           0.005615181                  NaN         9.984577e-03
## Lag 5e+05          -0.004204053                  NaN        -1.302465e-02
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.012835382
## Lag 2e+05         -0.001167879
## Lag 3e+05         -0.005758339
## Lag 4e+05          0.004927961
## Lag 5e+05          0.008854732
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.001513901           0.005874539          -0.006091326
## Lag 2e+05  0.033282908           0.021125859          -0.011534700
## Lag 3e+05  0.005796159          -0.005625543           0.006531761
## Lag 4e+05 -0.005969890          -0.010605392           0.018051583
## Lag 5e+05  0.007489625           0.009321633           0.002724554
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05          -0.025788213                  NaN          0.011286046
## Lag 2e+05           0.017441786                  NaN          0.005402040
## Lag 3e+05          -0.026582428                  NaN         -0.023272345
## Lag 4e+05          -0.014636136                  NaN          0.028877717
## Lag 5e+05          -0.005894719                  NaN          0.003447072
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.019623596
## Lag 2e+05          0.021227037
## Lag 3e+05          0.016545151
## Lag 4e+05         -0.018815185
## Lag 5e+05          0.003545777
## Chain 7 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05  0.01434024           0.004626503          -0.017021639
## Lag 2e+05 -0.01476897          -0.027135200           0.028141375
## Lag 3e+05 -0.03080730          -0.031366294          -0.025696652
## Lag 4e+05  0.01072404           0.020682187          -0.001998814
## Lag 5e+05  0.00959724           0.019394000           0.016012455
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN          1.000000000
## Lag 1e+05           0.031523119                  NaN         -0.006203119
## Lag 2e+05          -0.001869240                  NaN         -0.017257842
## Lag 3e+05          -0.016275732                  NaN          0.012030352
## Lag 4e+05           0.009931136                  NaN         -0.004597282
## Lag 5e+05          -0.001119268                  NaN         -0.019603059
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.019497629
## Lag 2e+05         -0.027067109
## Lag 3e+05          0.002886415
## Lag 4e+05         -0.007819070
## Lag 5e+05          0.008209035
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.012112947            0.03390803           0.021788021
## Lag 2e+05 -0.013687213           -0.01605194          -0.044347256
## Lag 3e+05  0.025312496            0.02074258           0.007723428
## Lag 4e+05  0.004385529            0.01633225           0.032585810
## Lag 5e+05 -0.009825275           -0.01775431           0.010566571
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000                  NaN         1.000000e+00
## Lag 1e+05          -0.008674521                  NaN        -7.898772e-03
## Lag 2e+05           0.001154270                  NaN         1.344878e-02
## Lag 3e+05          -0.004966047                  NaN        -3.771578e-04
## Lag 4e+05           0.047970901                  NaN         5.632767e-05
## Lag 5e+05          -0.007985781                  NaN        -1.982353e-02
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.012142338
## Lag 2e+05         -0.007916641
## Lag 3e+05          0.026838489
## Lag 4e+05          0.002808027
## Lag 5e+05         -0.000157396
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.1026                1.5100                0.3990 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.2370                   NaN                0.7180 
##  nodematch.race..wa.O 
##               -0.1190 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.9182964             0.1310373             0.6899214 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.8126266                   NaN             0.4727737 
##  nodematch.race..wa.O 
##             0.9053081 
## Joint P-value (lower = worse):  0.5639033 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                1.7068                0.9596                1.8423 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.3662                   NaN                0.8566 
##  nodematch.race..wa.O 
##                0.5856 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.08786123            0.33727499            0.06542821 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.17188794                   NaN            0.39166285 
##  nodematch.race..wa.O 
##            0.55816312 
## Joint P-value (lower = worse):  0.5021129 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.6560               -2.1425               -0.7895 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.2069                   NaN               -0.6992 
##  nodematch.race..wa.O 
##               -1.8992 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.09772466            0.03215543            0.42982988 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.83608788                   NaN            0.48444197 
##  nodematch.race..wa.O 
##            0.05753340 
## Joint P-value (lower = worse):  0.3762362 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                1.1273               -0.5726                2.2256 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.1670                   NaN                0.6328 
##  nodematch.race..wa.O 
##                0.6381 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.25960992            0.56691704            0.02603771 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.86738998                   NaN            0.52686028 
##  nodematch.race..wa.O 
##            0.52343552 
## Joint P-value (lower = worse):  0.1484366 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.34113               1.10827               1.64845 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.76016                   NaN               0.59144 
##  nodematch.race..wa.O 
##              -0.07205 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7330063             0.2677447             0.0992612 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.4471617                   NaN             0.5542288 
##  nodematch.race..wa.O 
##             0.9425598 
## Joint P-value (lower = worse):  0.3701823 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                1.5040                0.7083                1.8790 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.1594                   NaN               -1.0944 
##  nodematch.race..wa.O 
##                0.6603 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.1325868             0.4787634             0.0602406 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.8733316                   NaN             0.2737702 
##  nodematch.race..wa.O 
##             0.5090607 
## Joint P-value (lower = worse):  0.3780885 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.09589              -0.89212              -0.13076 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.21724                   NaN              -1.57929 
##  nodematch.race..wa.O 
##              -0.33912 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.9236099             0.3723267             0.8959632 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.8280217                   NaN             0.1142703 
##  nodematch.race..wa.O 
##             0.7345201 
## Joint P-value (lower = worse):  0.6819906 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.8785               -2.7077               -1.9445 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.1622                   NaN               -0.9914 
##  nodematch.race..wa.O 
##               -0.2855 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.37966794            0.00677590            0.05183046 
## nodefactor.race..wa.H  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.87114910                   NaN            0.32150661 
##  nodematch.race..wa.O 
##            0.77523173 
## Joint P-value (lower = worse):  0.1502384 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 5

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                            Mean     SD Naive SE Time-series SE
## edges                 -35.75960 40.288  0.23260        0.23222
## nodefactor.deg.main.1   0.07203 45.393  0.26208        0.26097
## nodefactor.race..wa.B  36.69990 15.670  0.09047        0.09047
## nodefactor.race..wa.H  36.38233 19.962  0.11525        0.11566
## nodefactor.region.EW   -0.08000 18.912  0.10919        0.10905
## nodefactor.region.OW    1.07807 36.490  0.21067        0.20794
## nodematch.race..wa.B   -8.47681  0.000  0.00000        0.00000
## nodematch.race..wa.H  -36.45184  3.805  0.02197        0.02201
## nodematch.race..wa.O   36.98905 33.244  0.19193        0.19264
## 
## 2. Quantiles for each variable:
## 
##                           2.5%     25%      50%     75%   97.5%
## edges                 -114.000 -63.000 -36.0000  -9.000  44.000
## nodefactor.deg.main.1  -88.000 -31.000   0.0000  30.000  89.000
## nodefactor.race..wa.B    6.835  25.835  36.8352  46.835  67.835
## nodefactor.race..wa.H   -1.908  23.092  36.0920  50.092  76.092
## nodefactor.region.EW   -36.680 -12.680  -0.6796  12.320  37.320
## nodefactor.region.OW   -70.548 -23.548   0.4520  25.452  73.452
## nodematch.race..wa.B    -8.477  -8.477  -8.4768  -8.477  -8.477
## nodematch.race..wa.H   -43.200 -39.200 -36.1997 -34.200 -28.200
## nodematch.race..wa.O   -27.844  14.156  37.1558  59.156 102.156
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.76054818
## nodefactor.deg.main.1 0.76054818            1.00000000
## nodefactor.race..wa.B 0.35940838            0.24348279
## nodefactor.race..wa.H 0.42896961            0.34432058
## nodefactor.region.EW  0.40354588            0.30746177
## nodefactor.region.OW  0.66468989            0.46226660
## nodematch.race..wa.B          NA                    NA
## nodematch.race..wa.H  0.07902052            0.07157873
## nodematch.race..wa.O  0.79392721            0.60836073
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                           0.359408384           0.428969608
## nodefactor.deg.main.1           0.243482789           0.344320581
## nodefactor.race..wa.B           1.000000000          -0.004141117
## nodefactor.race..wa.H          -0.004141117           1.000000000
## nodefactor.region.EW            0.087944757           0.265898248
## nodefactor.region.OW            0.220759040           0.267902595
## nodematch.race..wa.B                     NA                    NA
## nodematch.race..wa.H            0.004055651           0.354335329
## nodematch.race..wa.O           -0.032842076          -0.038115228
##                       nodefactor.region.EW nodefactor.region.OW
## edges                           0.40354588           0.66468989
## nodefactor.deg.main.1           0.30746177           0.46226660
## nodefactor.race..wa.B           0.08794476           0.22075904
## nodefactor.race..wa.H           0.26589825           0.26790259
## nodefactor.region.EW            1.00000000           0.12370677
## nodefactor.region.OW            0.12370677           1.00000000
## nodematch.race..wa.B                    NA                   NA
## nodematch.race..wa.H            0.07120527           0.04788087
## nodematch.race..wa.O            0.29607866           0.54607823
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                                   NA          0.079020518
## nodefactor.deg.main.1                   NA          0.071578726
## nodefactor.race..wa.B                   NA          0.004055651
## nodefactor.race..wa.H                   NA          0.354335329
## nodefactor.region.EW                    NA          0.071205273
## nodefactor.region.OW                    NA          0.047880874
## nodematch.race..wa.B                     1                   NA
## nodematch.race..wa.H                    NA          1.000000000
## nodematch.race..wa.O                    NA         -0.004468835
##                       nodematch.race..wa.O
## edges                          0.793927212
## nodefactor.deg.main.1          0.608360727
## nodefactor.race..wa.B         -0.032842076
## nodefactor.race..wa.H         -0.038115228
## nodefactor.region.EW           0.296078656
## nodefactor.region.OW           0.546078228
## nodematch.race..wa.B                    NA
## nodematch.race..wa.H          -0.004468835
## nodematch.race..wa.O           1.000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05 -0.006920140          0.0007543802          -0.007726221
## Lag 2e+05 -0.001562634          0.0113863704          -0.003760960
## Lag 3e+05  0.014590096         -0.0020609688          -0.004835891
## Lag 4e+05  0.016158739          0.0087172971          -0.029290893
## Lag 5e+05 -0.018248830          0.0010250134          -0.018565737
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.003247102         0.0008359808          0.002073049
## Lag 2e+05          -0.014925791         0.0242954052         -0.008063595
## Lag 3e+05          -0.017800445         0.0322595483          0.017994280
## Lag 4e+05           0.014905960        -0.0257610793         -0.011515614
## Lag 5e+05          -0.027213326        -0.0057712815         -0.007513095
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.006795651          0.001279202
## Lag 2e+05                  NaN          0.024304177         -0.023386609
## Lag 3e+05                  NaN         -0.019813275          0.022353223
## Lag 4e+05                  NaN         -0.006022160          0.022087185
## Lag 5e+05                  NaN          0.042762637          0.001667387
## Chain 2 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.000000e+00
## Lag 1e+05 -0.006307659          -0.002279447         -8.968465e-03
## Lag 2e+05 -0.007894586           0.007084864         -1.478297e-02
## Lag 3e+05 -0.014990607          -0.029592100          3.166262e-03
## Lag 4e+05 -0.004174385           0.008500037         -1.049888e-03
## Lag 5e+05  0.013633641           0.014201471         -4.773963e-05
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.004092067         0.0028398898         -0.029271045
## Lag 2e+05           0.004982865        -0.0001421547         -0.012415471
## Lag 3e+05          -0.038941511         0.0094561546         -0.033068812
## Lag 4e+05           0.004542032         0.0203550964          0.003825339
## Lag 5e+05          -0.003433483         0.0175285931          0.020344146
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.023040502          0.006601975
## Lag 2e+05                  NaN          0.009392134         -0.028008959
## Lag 3e+05                  NaN         -0.004844628         -0.008093553
## Lag 4e+05                  NaN         -0.002291789         -0.008972590
## Lag 5e+05                  NaN         -0.010806979          0.022959398
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.020851445          0.0213449240           0.021002203
## Lag 2e+05 -0.015201339          0.0002802347          -0.005622173
## Lag 3e+05  0.001101881         -0.0009375464           0.007344251
## Lag 4e+05  0.009078094          0.0105087641           0.019429328
## Lag 5e+05  0.007257310          0.0062699208          -0.006188380
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000           1.00000000
## Lag 1e+05          -0.014542523        -0.0155730882           0.02016988
## Lag 2e+05           0.009116621         0.0007449845          -0.01315595
## Lag 3e+05          -0.001587001        -0.0180583041           0.01369896
## Lag 4e+05           0.011902681        -0.0018120151           0.01221622
## Lag 5e+05           0.021365439         0.0133568239           0.02043736
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.008616910          0.019456869
## Lag 2e+05                  NaN          0.004765738         -0.014249017
## Lag 3e+05                  NaN          0.021865753         -0.008100761
## Lag 4e+05                  NaN         -0.022507011          0.001454497
## Lag 5e+05                  NaN          0.015920579          0.010156596
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000            1.00000000            1.00000000
## Lag 1e+05  0.021892723            0.01308473            0.01998203
## Lag 2e+05 -0.000447345            0.01426082           -0.01112593
## Lag 3e+05 -0.016054171           -0.04103977           -0.01503728
## Lag 4e+05  0.001456830            0.01179998           -0.03268493
## Lag 5e+05 -0.045034925           -0.02580277           -0.01848709
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.000000e+00          1.000000000          1.000000000
## Lag 1e+05         -1.718532e-02          0.004451530         -0.013626883
## Lag 2e+05          1.308271e-02         -0.005608763          0.009850617
## Lag 3e+05          8.844331e-05         -0.016787812         -0.015148656
## Lag 4e+05          2.248276e-02          0.035707193          0.001338650
## Lag 5e+05          1.086732e-02          0.011477575         -0.032304884
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.005146971          0.025112935
## Lag 2e+05                  NaN         -0.006337931         -0.001979264
## Lag 3e+05                  NaN         -0.004903854          0.004386690
## Lag 4e+05                  NaN          0.020982723         -0.011081073
## Lag 5e+05                  NaN         -0.016283933         -0.036330387
## Chain 5 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05  0.01100127          -0.002755822           0.020389338
## Lag 2e+05 -0.03394009          -0.018456998          -0.005972124
## Lag 3e+05 -0.00765711           0.007680960           0.013632004
## Lag 4e+05  0.01150824          -0.034443461           0.012439760
## Lag 5e+05  0.01100247           0.011296929          -0.016254922
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.006132266         -0.012508750          0.023840794
## Lag 2e+05           0.006098205          0.022392914         -0.041903018
## Lag 3e+05          -0.020508693         -0.020817556          0.001440925
## Lag 4e+05          -0.010138528         -0.003203452          0.026879674
## Lag 5e+05           0.003348459          0.017626430          0.006804279
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.009551700          0.022784676
## Lag 2e+05                  NaN         -0.004433462         -0.047491945
## Lag 3e+05                  NaN         -0.009170469         -0.012520830
## Lag 4e+05                  NaN          0.002449292          0.004751113
## Lag 5e+05                  NaN          0.018986048         -0.008281171
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.026259261          0.0001329232          -0.008128521
## Lag 2e+05 -0.002028759          0.0195560600           0.008275359
## Lag 3e+05 -0.013039523          0.0037433904          -0.024489054
## Lag 4e+05  0.031290776          0.0236331563           0.007937543
## Lag 5e+05  0.025598242          0.0206769730           0.001854600
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.009392497          0.011288309         -0.004395589
## Lag 2e+05          -0.028477280         -0.034692172          0.003138002
## Lag 3e+05          -0.003354733         -0.005619677         -0.006966524
## Lag 4e+05           0.030775161          0.010482577          0.039623330
## Lag 5e+05          -0.004706714          0.002322290         -0.009895477
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.020124386          0.014251571
## Lag 2e+05                  NaN          0.007609056          0.003889284
## Lag 3e+05                  NaN          0.019326728          0.002111854
## Lag 4e+05                  NaN          0.026975149          0.042443957
## Lag 5e+05                  NaN         -0.004949391          0.012206703
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000            1.00000000
## Lag 1e+05  0.029002108          0.0186322432           -0.02234278
## Lag 2e+05  0.024991968          0.0199677327            0.01727104
## Lag 3e+05  0.004026168         -0.0003743267            0.01659402
## Lag 4e+05 -0.005388663         -0.0005958859            0.01314381
## Lag 5e+05 -0.036499215         -0.0041121585           -0.01070611
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000         1.0000000000          1.000000000
## Lag 1e+05          0.0404599422         0.0226547088          0.003442628
## Lag 2e+05         -0.0083028263         0.0004761757          0.007009784
## Lag 3e+05         -0.0002298428        -0.0006464667          0.007044039
## Lag 4e+05         -0.0118833996        -0.0076669732          0.019547146
## Lag 5e+05          0.0179819985        -0.0279571785         -0.014836038
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.0000000000         1.0000000000
## Lag 1e+05                  NaN         0.0071635955         0.0262006842
## Lag 2e+05                  NaN        -0.0005469372         0.0225552223
## Lag 3e+05                  NaN         0.0007284861        -0.0014688764
## Lag 4e+05                  NaN        -0.0100947523        -0.0003398162
## Lag 5e+05                  NaN         0.0206805579        -0.0056920144
## Chain 8 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05  0.01566956           0.020796015           0.011766582
## Lag 2e+05 -0.03146633          -0.005563645          -0.013993795
## Lag 3e+05 -0.03098937          -0.036833032           0.018878352
## Lag 4e+05 -0.02663669           0.001597165          -0.015789391
## Lag 5e+05 -0.02482600          -0.004999028          -0.005091651
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05           0.015481640         -0.023957423        -0.0056389735
## Lag 2e+05           0.011727266          0.002944722        -0.0270033556
## Lag 3e+05          -0.041436984          0.006908259        -0.0076949757
## Lag 4e+05           0.004852107         -0.035106985        -0.0002691687
## Lag 5e+05           0.003576914         -0.005835384        -0.0291163356
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.023250011          0.001320633
## Lag 2e+05                  NaN         -0.017450425         -0.032472670
## Lag 3e+05                  NaN         -0.014707651          0.001535080
## Lag 4e+05                  NaN          0.026714606         -0.030742711
## Lag 5e+05                  NaN         -0.008641063         -0.005867381
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              0.407900              0.310278              1.021049 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             -0.057528             -1.252419              0.565962 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             -0.917209             -0.006474 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6833471             0.7563497             0.3072314 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.9541248             0.2104172             0.5714195 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.3590329             0.9948344 
## Joint P-value (lower = worse):  0.9059805 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.81122               1.07426              -0.02656 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               1.28799               1.75549               0.10587 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              -1.41508               0.11947 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.41723758            0.28270758            0.97881418 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.19774985            0.07917537            0.91568218 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.15704416            0.90490508 
## Joint P-value (lower = worse):  0.4556301 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               1.74307               1.79392              -0.65422 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.79928               0.34196               0.00639 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              -0.80946               1.77667 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.08132188            0.07282635            0.51297059 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.42413035            0.73238230            0.99490160 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.41824854            0.07562257 
## Joint P-value (lower = worse):  0.3374752 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                1.6132                1.9608               -0.5088 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.9554                1.7994               -0.4040 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.7759                1.6318 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.10669816            0.04989781            0.61086855 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.33935317            0.07196071            0.68617668 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.43777936            0.10272718 
## Joint P-value (lower = worse):  0.2417018 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.72487               0.32631              -0.77385 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.45828              -1.18819              -0.70363 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               0.04267              -0.20822 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.4685301             0.7441868             0.4390219 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.6467527             0.2347598             0.4816640 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.9659669             0.8350552 
## Joint P-value (lower = worse):  0.8868549 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.4980               -1.5332               -1.3485 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.6193                1.8091               -0.7740 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               -0.4237               -0.5529 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.61845051            0.12521699            0.17748197 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.53574750            0.07043814            0.43890678 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.67176905            0.58034299 
## Joint P-value (lower = worse):  0.2908849 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.53914              -0.15908              -1.03425 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.08188               1.19952               0.63381 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               1.53474               0.01412 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.5897928             0.8736062             0.3010204 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.9347394             0.2303240             0.5262057 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.1248471             0.9887326 
## Joint P-value (lower = worse):  0.3727964 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.38699              -0.46981              -0.92253 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.07887              -1.29674               0.23852 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              -1.32746              -0.11240 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6987641             0.6384942             0.3562497 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.9371392             0.1947198             0.8114782 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.1843551             0.9105094 
## Joint P-value (lower = worse):  0.7843281 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 6

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -37.3340 39.919  0.23047        0.23142
## nodefactor.deg.main.1  -0.8270 45.266  0.26134        0.26135
## nodefactor.race..wa.B  36.3643 15.695  0.09061        0.08997
## nodefactor.race..wa.H  36.6297 20.025  0.11561        0.11536
## nodefactor.region.EW    0.1932 18.887  0.10905        0.10807
## nodefactor.region.OW   -1.4275 36.680  0.21177        0.21177
## nodematch.race..wa.B   -8.4768  0.000  0.00000        0.00000
## nodematch.race..wa.H  -36.3424  3.825  0.02208        0.02226
## nodematch.race..wa.O   35.6122 32.858  0.18970        0.18954
## absdiff.sqrt.age        0.3369 45.584  0.26318        0.26318
## 
## 2. Quantiles for each variable:
## 
##                           2.5%     25%      50%     75%   97.5%
## edges                 -116.000 -64.000 -37.0000 -10.000  41.000
## nodefactor.deg.main.1  -89.000 -31.000  -1.0000  30.000  88.000
## nodefactor.race..wa.B    5.835  25.835  35.8352  46.835  66.835
## nodefactor.race..wa.H   -1.908  23.092  36.0920  50.092  76.092
## nodefactor.region.EW   -36.680 -12.680   0.3204  12.320  38.320
## nodefactor.region.OW   -72.548 -26.548  -1.5480  23.452  69.452
## nodematch.race..wa.B    -8.477  -8.477  -8.4768  -8.477  -8.477
## nodematch.race..wa.H   -43.200 -39.200 -36.1997 -34.200 -28.200
## nodematch.race..wa.O   -27.844  13.156  35.1558  58.156 100.156
## absdiff.sqrt.age       -88.949 -30.698   0.3668  31.252  89.851
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                            edges nodefactor.deg.main.1
## edges                 1.00000000            0.75772330
## nodefactor.deg.main.1 0.75772330            1.00000000
## nodefactor.race..wa.B 0.35646179            0.23583839
## nodefactor.race..wa.H 0.43391070            0.35007220
## nodefactor.region.EW  0.39494391            0.29726957
## nodefactor.region.OW  0.66870232            0.46884798
## nodematch.race..wa.B          NA                    NA
## nodematch.race..wa.H  0.08253419            0.06452848
## nodematch.race..wa.O  0.78979489            0.60206914
## absdiff.sqrt.age      0.73908669            0.55921788
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                            0.35646179            0.43391070
## nodefactor.deg.main.1            0.23583839            0.35007220
## nodefactor.race..wa.B            1.00000000           -0.01155710
## nodefactor.race..wa.H           -0.01155710            1.00000000
## nodefactor.region.EW             0.07911717            0.27202956
## nodefactor.region.OW             0.22397885            0.27496522
## nodematch.race..wa.B                     NA                    NA
## nodematch.race..wa.H             0.01201325            0.33918919
## nodematch.race..wa.O            -0.03615516           -0.03727785
## absdiff.sqrt.age                 0.26569426            0.32306807
##                       nodefactor.region.EW nodefactor.region.OW
## edges                           0.39494391           0.66870232
## nodefactor.deg.main.1           0.29726957           0.46884798
## nodefactor.race..wa.B           0.07911717           0.22397885
## nodefactor.race..wa.H           0.27202956           0.27496522
## nodefactor.region.EW            1.00000000           0.12013726
## nodefactor.region.OW            0.12013726           1.00000000
## nodematch.race..wa.B                    NA                   NA
## nodematch.race..wa.H            0.06931307           0.05450349
## nodematch.race..wa.O            0.28430823           0.54418877
## absdiff.sqrt.age                0.28438448           0.49709396
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                                   NA          0.082534195
## nodefactor.deg.main.1                   NA          0.064528481
## nodefactor.race..wa.B                   NA          0.012013254
## nodefactor.race..wa.H                   NA          0.339189187
## nodefactor.region.EW                    NA          0.069313070
## nodefactor.region.OW                    NA          0.054503486
## nodematch.race..wa.B                     1                   NA
## nodematch.race..wa.H                    NA          1.000000000
## nodematch.race..wa.O                    NA          0.004223227
## absdiff.sqrt.age                        NA          0.060399431
##                       nodematch.race..wa.O absdiff.sqrt.age
## edges                          0.789794893       0.73908669
## nodefactor.deg.main.1          0.602069142       0.55921788
## nodefactor.race..wa.B         -0.036155157       0.26569426
## nodefactor.race..wa.H         -0.037277854       0.32306807
## nodefactor.region.EW           0.284308226       0.28438448
## nodefactor.region.OW           0.544188773       0.49709396
## nodematch.race..wa.B                    NA               NA
## nodematch.race..wa.H           0.004223227       0.06039943
## nodematch.race..wa.O           1.000000000       0.58114329
## absdiff.sqrt.age               0.581143292       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.000000e+00
## Lag 1e+05  0.001180621          -0.006334552         -7.751064e-05
## Lag 2e+05 -0.005495008          -0.007128924          1.047376e-02
## Lag 3e+05 -0.011202157          -0.022554241          9.792288e-03
## Lag 4e+05 -0.004633560           0.017138440         -1.446275e-02
## Lag 5e+05  0.018662549           0.025788613          8.652062e-03
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.006491597         -0.006710416          0.007916870
## Lag 2e+05           0.008979006         -0.005096989         -0.003725218
## Lag 3e+05          -0.004130969         -0.036570794         -0.010402986
## Lag 4e+05          -0.008232362          0.020522750         -0.004969289
## Lag 5e+05           0.014750679          0.004755628         -0.016866358
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000         1.0000000000
## Lag 1e+05                  NaN          0.003836159        -0.0130444790
## Lag 2e+05                  NaN          0.044524794        -0.0011114488
## Lag 3e+05                  NaN          0.034205413         0.0067570291
## Lag 4e+05                  NaN         -0.011970927        -0.0007641154
## Lag 5e+05                  NaN         -0.011838533         0.0057273655
##           absdiff.sqrt.age
## Lag 0         1.0000000000
## Lag 1e+05     0.0144129601
## Lag 2e+05    -0.0091254866
## Lag 3e+05    -0.0063831598
## Lag 4e+05     0.0013312372
## Lag 5e+05     0.0005517172
## Chain 2 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000          1.0000000000
## Lag 1e+05 -0.012912010         -0.0082080567          0.0110533897
## Lag 2e+05 -0.015934359         -0.0003417295         -0.0008891257
## Lag 3e+05  0.009705569         -0.0092575436          0.0234499021
## Lag 4e+05 -0.012405746         -0.0127103253          0.0299248014
## Lag 5e+05 -0.009162732         -0.0056856012          0.0143443579
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000           1.00000000
## Lag 1e+05          -0.022852357         -0.001724069          -0.01353271
## Lag 2e+05          -0.003198268         -0.003385084          -0.00016209
## Lag 3e+05           0.008252763         -0.007967320           0.01529012
## Lag 4e+05          -0.001051809          0.011779988          -0.01570769
## Lag 5e+05           0.007581213         -0.004883294          -0.01764065
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.004885881         -0.022121515
## Lag 2e+05                  NaN          0.002159978         -0.019081114
## Lag 3e+05                  NaN         -0.011924880          0.035227439
## Lag 4e+05                  NaN          0.005658210         -0.003669075
## Lag 5e+05                  NaN          0.002027072         -0.019524938
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.007961599
## Lag 2e+05     -0.003763338
## Lag 3e+05      0.013104194
## Lag 4e+05     -0.006292490
## Lag 5e+05     -0.014121989
## Chain 3 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0149773221          -0.006537942          -0.022102789
## Lag 2e+05  0.0077752232          -0.020096844           0.021053704
## Lag 3e+05  0.0004472099          -0.013254436          -0.006449229
## Lag 4e+05  0.0080082787          -0.021173883          -0.012994095
## Lag 5e+05 -0.0079862478          -0.017027930           0.036040640
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000          1.000000000          1.000000000
## Lag 1e+05           -0.00273928         -0.002263771         -0.003402030
## Lag 2e+05           -0.01367584          0.005588768          0.016133681
## Lag 3e+05            0.01218998          0.027117655         -0.006525887
## Lag 4e+05            0.02514214         -0.006989485          0.029786577
## Lag 5e+05           -0.01614954         -0.016563445         -0.003125664
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN          0.013414263         -0.004911775
## Lag 2e+05                  NaN         -0.012504768          0.016389496
## Lag 3e+05                  NaN         -0.004550009          0.005713338
## Lag 4e+05                  NaN          0.011007036         -0.003797593
## Lag 5e+05                  NaN          0.020038116         -0.034822330
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.015724482
## Lag 2e+05     -0.006246153
## Lag 3e+05      0.010622559
## Lag 4e+05     -0.007835825
## Lag 5e+05      0.004464919
## Chain 4 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000            1.00000000
## Lag 1e+05 -0.0056979711          -0.002851530            0.03864994
## Lag 2e+05 -0.0052584088          -0.011004537           -0.00143329
## Lag 3e+05  0.0141416703           0.014538062           -0.01025144
## Lag 4e+05  0.0008867851          -0.009513295           -0.00914985
## Lag 5e+05 -0.0147437168          -0.030827409            0.03281718
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.024034416          0.020854945          0.006740192
## Lag 2e+05          -0.001505770          0.013143455          0.006005972
## Lag 3e+05           0.017519357          0.003655047          0.017343411
## Lag 4e+05          -0.008112655          0.028892163         -0.036429019
## Lag 5e+05          -0.008609845          0.003884061         -0.006683451
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.0000000000          1.000000000
## Lag 1e+05                  NaN         0.0005575539          0.004407273
## Lag 2e+05                  NaN        -0.0181172212          0.004507011
## Lag 3e+05                  NaN         0.0326183613          0.010708062
## Lag 4e+05                  NaN        -0.0207992325          0.001271791
## Lag 5e+05                  NaN        -0.0122747376         -0.008473650
##           absdiff.sqrt.age
## Lag 0         1.0000000000
## Lag 1e+05    -0.0116643826
## Lag 2e+05    -0.0176631865
## Lag 3e+05     0.0140838151
## Lag 4e+05     0.0007270239
## Lag 5e+05    -0.0153794710
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000            1.00000000
## Lag 1e+05 -0.005013070          -0.004014229           -0.02389439
## Lag 2e+05 -0.002397745           0.008417129           -0.02019361
## Lag 3e+05 -0.007176281           0.021247209            0.01179908
## Lag 4e+05 -0.014252559          -0.014550921           -0.03751960
## Lag 5e+05  0.009642238           0.006491331           -0.01228158
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.004324964          0.004980467          0.009341976
## Lag 2e+05           0.002422719         -0.011358198          0.025882355
## Lag 3e+05           0.003402381          0.014208037         -0.010361145
## Lag 4e+05           0.012343520         -0.019335307         -0.031341414
## Lag 5e+05           0.007667633          0.007663114          0.001596306
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.001021210          0.004312474
## Lag 2e+05                  NaN         -0.034941402         -0.003642870
## Lag 3e+05                  NaN         -0.016936044         -0.012128086
## Lag 4e+05                  NaN          0.012309230         -0.008052318
## Lag 5e+05                  NaN          0.008219758          0.004607952
##           absdiff.sqrt.age
## Lag 0         1.0000000000
## Lag 1e+05    -0.0006528685
## Lag 2e+05     0.0038058520
## Lag 3e+05    -0.0195058440
## Lag 4e+05    -0.0115658915
## Lag 5e+05     0.0079897421
## Chain 6 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000          1.0000000000          1.0000000000
## Lag 1e+05  0.0025884725         -0.0140488990          0.0005955699
## Lag 2e+05 -0.0127109683         -0.0184398164          0.0031510305
## Lag 3e+05  0.0044891171          0.0002638759          0.0199405883
## Lag 4e+05  0.0006152233         -0.0029112754          0.0038404384
## Lag 5e+05  0.0249596607          0.0094479002          0.0011165588
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.012323604         -0.002136137         -0.015383430
## Lag 2e+05           0.001168489          0.010575831         -0.012683980
## Lag 3e+05          -0.014349899         -0.004907547         -0.013341649
## Lag 4e+05          -0.019413873          0.020792408          0.002532154
## Lag 5e+05           0.013116229         -0.003251437          0.021342811
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN         1.0000000000         1.0000000000
## Lag 1e+05                  NaN         0.0398067104         0.0008116609
## Lag 2e+05                  NaN         0.0046019488         0.0036796228
## Lag 3e+05                  NaN        -0.0346775115         0.0056297852
## Lag 4e+05                  NaN         0.0001525984         0.0055476295
## Lag 5e+05                  NaN         0.0010864826         0.0127447322
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.001946528
## Lag 2e+05     -0.000171540
## Lag 3e+05      0.002327198
## Lag 4e+05     -0.027144308
## Lag 5e+05     -0.007599620
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.006838309         -0.0004407087           0.026043904
## Lag 2e+05  0.021659020          0.0028538371           0.017137087
## Lag 3e+05 -0.029673243         -0.0075366136           0.001859680
## Lag 4e+05  0.006267427         -0.0421948094          -0.034964980
## Lag 5e+05  0.002657771         -0.0256905512          -0.005743912
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.010735651         -0.013148143          0.020942633
## Lag 2e+05          -0.017731373         -0.012725009          0.003963117
## Lag 3e+05          -0.028333577         -0.050872935          0.002998686
## Lag 4e+05           0.017440135         -0.008861462         -0.006893772
## Lag 5e+05           0.006833705          0.016905315         -0.012018564
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000         1.0000000000
## Lag 1e+05                  NaN         -0.008689809        -0.0123325912
## Lag 2e+05                  NaN          0.022216995         0.0162053362
## Lag 3e+05                  NaN         -0.022174708        -0.0217311058
## Lag 4e+05                  NaN         -0.015118393        -0.0006478394
## Lag 5e+05                  NaN          0.021451594         0.0124007020
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.016498205
## Lag 2e+05      0.012050335
## Lag 3e+05     -0.015261170
## Lag 4e+05      0.009813626
## Lag 5e+05      0.020198891
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05 -0.006062183          -0.015678086          0.0083105430
## Lag 2e+05  0.006708011          -0.024301796          0.0069158047
## Lag 3e+05  0.029722364           0.014832743         -0.0118971343
## Lag 4e+05 -0.018904125           0.001544208         -0.0218237872
## Lag 5e+05  0.001799924           0.019207273          0.0004042609
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.044219042         -0.020547888          0.012621231
## Lag 2e+05           0.023860559         -0.004274842          0.011252962
## Lag 3e+05          -0.009494200          0.009675042          0.018294448
## Lag 4e+05           0.019187717         -0.028486311         -0.005127968
## Lag 5e+05          -0.001881202          0.021546537          0.015994079
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0                      NaN          1.000000000          1.000000000
## Lag 1e+05                  NaN         -0.020244943         -0.018601924
## Lag 2e+05                  NaN          0.012116947          0.003664529
## Lag 3e+05                  NaN         -0.005738144          0.029644200
## Lag 4e+05                  NaN         -0.009694277         -0.004178620
## Lag 5e+05                  NaN         -0.009737966         -0.015342212
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.007822104
## Lag 2e+05      0.005986246
## Lag 3e+05     -0.006654198
## Lag 4e+05      0.022017511
## Lag 5e+05     -0.010208184
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.3175               -2.0607                0.1804 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -1.4403               -0.5961               -1.7997 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               -0.2008               -0.9201 
##      absdiff.sqrt.age 
##               -0.6460 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.18767872            0.03932994            0.85682794 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.14977960            0.55107791            0.07190995 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.84084621            0.35751807 
##      absdiff.sqrt.age 
##            0.51828309 
## Joint P-value (lower = worse):  0.7923288 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.34226               0.89225              -0.63897 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.23474               0.01799               0.08582 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              -0.66994               0.51600 
##      absdiff.sqrt.age 
##              -0.47904 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7321528             0.3722564             0.5228429 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8144100             0.9856458             0.9316080 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.5028960             0.6058556 
##      absdiff.sqrt.age 
##             0.6319123 
## Joint P-value (lower = worse):  0.9414097 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                1.6991                0.1333                1.2808 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.5350                1.6995                2.0221 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN               -1.5407                1.0108 
##      absdiff.sqrt.age 
##                1.0473 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.08929739            0.89396000            0.20027690 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.59268025            0.08922286            0.04316501 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.12338284            0.31210347 
##      absdiff.sqrt.age 
##            0.29493917 
## Joint P-value (lower = worse):  0.2237952 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.02243               0.38552               1.68264 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -2.61048              -0.25985              -1.70163 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              -1.62649               0.49682 
##      absdiff.sqrt.age 
##              -1.26781 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##           0.982107172           0.699853163           0.092444530 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##           0.009041569           0.794980741           0.088825395 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN           0.103845754           0.619314892 
##      absdiff.sqrt.age 
##           0.204866536 
## Joint P-value (lower = worse):  0.05389209 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               1.33619               1.08926               0.12498 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.64254               0.62688               0.72110 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              -0.66717               1.05827 
##      absdiff.sqrt.age 
##              -0.01831 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.1814856             0.2760386             0.9005356 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5205229             0.5307352             0.4708479 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN             0.5046646             0.2899331 
##      absdiff.sqrt.age 
##             0.9853903 
## Joint P-value (lower = worse):  0.8622414 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              1.167988              1.827731             -1.512398 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              0.711738              1.232957              1.314179 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN              0.005047              1.636631 
##      absdiff.sqrt.age 
##              0.143740 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.24281160            0.06758992            0.13043257 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.47662729            0.21759175            0.18878596 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.99597306            0.10170767 
##      absdiff.sqrt.age 
##            0.88570617 
## Joint P-value (lower = worse):  0.2610809 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.8070               -1.4013               -2.1040 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.5143               -0.2389               -0.1297 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.3610                0.4783 
##      absdiff.sqrt.age 
##               -0.3008 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.41967348            0.16111525            0.03537669 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.60706434            0.81119483            0.89678637 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.71812472            0.63243793 
##      absdiff.sqrt.age 
##            0.76359452 
## Joint P-value (lower = worse):  0.5127734 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -1.9379               -2.1068                0.2124 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.5172               -0.9660               -0.6145 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN                0.7762               -2.0746 
##      absdiff.sqrt.age 
##               -0.4365 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.05263505            0.03513120            0.83183241 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.60501069            0.33402658            0.53886321 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                   NaN            0.43765545            0.03802777 
##      absdiff.sqrt.age 
##            0.66248329 
## Joint P-value (lower = worse):  0.3984074 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 7

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                            Mean     SD Naive SE Time-series SE
## edges                 -35.47483 59.715  0.34476        0.36818
## nodefactor.deg.main.1   0.08127 61.932  0.35757        0.38070
## nodefactor.race..wa.B  36.74380 19.290  0.11137        0.11873
## nodefactor.race..wa.H  36.61330 24.968  0.14415        0.14912
## nodefactor.region.EW    0.10143 23.859  0.13775        0.14432
## nodefactor.region.OW    0.31780 48.874  0.28217        0.29177
## concurrent              0.70853 53.712  0.31010        0.33271
## nodematch.race..wa.B   -8.47681  0.000  0.00000        0.00000
## nodematch.race..wa.H  -36.42757  3.923  0.02265        0.02327
## nodematch.race..wa.O   37.02321 45.238  0.26118        0.27725
## absdiff.sqrt.age        0.71055 59.491  0.34347        0.36462
## 
## 2. Quantiles for each variable:
## 
##                            2.5%     25%        50%     75%   97.5%
## edges                 -152.0000 -76.000 -3.600e+01   5.000  83.000
## nodefactor.deg.main.1 -120.0000 -42.000  0.000e+00  41.000 123.000
## nodefactor.race..wa.B   -0.1648  23.835  3.684e+01  49.835  74.835
## nodefactor.race..wa.H  -10.9080  19.092  3.709e+01  53.092  86.092
## nodefactor.region.EW   -45.6796 -15.680  3.204e-01  16.320  47.320
## nodefactor.region.OW   -93.5480 -32.548  4.520e-01  32.452  97.452
## concurrent            -103.0000 -36.000 -2.274e-13  37.000 107.000
## nodematch.race..wa.B    -8.4768  -8.477 -8.477e+00  -8.477  -8.477
## nodematch.race..wa.H   -43.1997 -39.200 -3.620e+01 -34.200 -28.200
## nodematch.race..wa.O   -51.8442   6.156  3.716e+01  67.156 126.181
## absdiff.sqrt.age      -113.7449 -39.763  1.380e-01  40.465 118.729
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                           edges nodefactor.deg.main.1
## edges                 1.0000000             0.8232231
## nodefactor.deg.main.1 0.8232231             1.0000000
## nodefactor.race..wa.B 0.4389905             0.3290299
## nodefactor.race..wa.H 0.5167149             0.4412531
## nodefactor.region.EW  0.4795969             0.4017143
## nodefactor.region.OW  0.7422210             0.5828574
## concurrent            0.9582711             0.7876116
## nodematch.race..wa.B         NA                    NA
## nodematch.race..wa.H  0.1159713             0.1037311
## nodematch.race..wa.O  0.8576908             0.7118194
## absdiff.sqrt.age      0.8515697             0.6991526
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                           0.438990520            0.51671488
## nodefactor.deg.main.1           0.329029942            0.44125308
## nodefactor.race..wa.B           1.000000000            0.04844203
## nodefactor.race..wa.H           0.048442027            1.00000000
## nodefactor.region.EW            0.163517146            0.34220171
## nodefactor.region.OW            0.303603143            0.37304485
## concurrent                      0.423846564            0.49404754
## nodematch.race..wa.B                     NA                    NA
## nodematch.race..wa.H            0.004871687            0.40019043
## nodematch.race..wa.O            0.126746326            0.14418612
## absdiff.sqrt.age                0.375457333            0.44207943
##                       nodefactor.region.EW nodefactor.region.OW concurrent
## edges                            0.4795969           0.74222099  0.9582711
## nodefactor.deg.main.1            0.4017143           0.58285744  0.7876116
## nodefactor.race..wa.B            0.1635171           0.30360314  0.4238466
## nodefactor.race..wa.H            0.3422017           0.37304485  0.4940475
## nodefactor.region.EW             1.0000000           0.22857346  0.4568735
## nodefactor.region.OW             0.2285735           1.00000000  0.7066057
## concurrent                       0.4568735           0.70660575  1.0000000
## nodematch.race..wa.B                    NA                   NA         NA
## nodematch.race..wa.H             0.1043839           0.08495395  0.1132865
## nodematch.race..wa.O             0.3835298           0.65175550  0.8213435
## absdiff.sqrt.age                 0.3992235           0.63144313  0.8124875
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                                   NA          0.115971271
## nodefactor.deg.main.1                   NA          0.103731125
## nodefactor.race..wa.B                   NA          0.004871687
## nodefactor.race..wa.H                   NA          0.400190428
## nodefactor.region.EW                    NA          0.104383944
## nodefactor.region.OW                    NA          0.084953954
## concurrent                              NA          0.113286520
## nodematch.race..wa.B                     1                   NA
## nodematch.race..wa.H                    NA          1.000000000
## nodematch.race..wa.O                    NA          0.016838978
## absdiff.sqrt.age                        NA          0.095243622
##                       nodematch.race..wa.O absdiff.sqrt.age
## edges                           0.85769081       0.85156967
## nodefactor.deg.main.1           0.71181936       0.69915264
## nodefactor.race..wa.B           0.12674633       0.37545733
## nodefactor.race..wa.H           0.14418612       0.44207943
## nodefactor.region.EW            0.38352982       0.39922349
## nodefactor.region.OW            0.65175550       0.63144313
## concurrent                      0.82134351       0.81248755
## nodematch.race..wa.B                    NA               NA
## nodematch.race..wa.H            0.01683898       0.09524362
## nodematch.race..wa.O            1.00000000       0.72824792
## absdiff.sqrt.age                0.72824792       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000            1.00000000           1.000000000
## Lag 1e+05  0.08077509            0.05113659           0.085527807
## Lag 2e+05  0.01330447            0.01793462          -0.010421904
## Lag 3e+05  0.04236702            0.04801589          -0.012435669
## Lag 4e+05 -0.00713260            0.02806275          -0.006880992
## Lag 5e+05  0.02815143            0.03531967           0.007366477
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05          0.0464385762          0.041252377          0.045540577
## Lag 2e+05          0.0133772262          0.015429696         -0.009230566
## Lag 3e+05          0.0009941337         -0.015621490          0.020869210
## Lag 4e+05          0.0104713530         -0.004984904         -0.018109919
## Lag 5e+05          0.0075032513          0.011746854          0.028070267
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.00000000                  NaN         1.0000000000
## Lag 1e+05  0.09139123                  NaN        -0.0066558523
## Lag 2e+05  0.01931918                  NaN        -0.0154258131
## Lag 3e+05  0.03398606                  NaN        -0.0057995023
## Lag 4e+05 -0.01051710                  NaN        -0.0008811985
## Lag 5e+05  0.02455403                  NaN         0.0098880972
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000     1.0000000000
## Lag 1e+05          0.062112778     0.0381992309
## Lag 2e+05          0.009279785     0.0112810549
## Lag 3e+05          0.046929187     0.0428568832
## Lag 4e+05          0.014995513    -0.0005664862
## Lag 5e+05          0.031414809     0.0493078320
## Chain 2 
##                   edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0671009349           0.054166048           0.076364629
## Lag 2e+05  0.0211990775           0.008002542           0.013247647
## Lag 3e+05  0.0092064357           0.008621436          -0.003797904
## Lag 4e+05  0.0094416617           0.004339443           0.012819059
## Lag 5e+05 -0.0007135301          -0.011401890          -0.013212008
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000           1.00000000
## Lag 1e+05           0.068797658          0.050222674           0.06964077
## Lag 2e+05          -0.005332033          0.005305532           0.02523226
## Lag 3e+05           0.009512394         -0.004124216          -0.01336901
## Lag 4e+05          -0.005891607          0.007450193           0.00900467
## Lag 5e+05           0.002494312          0.030389079           0.01195871
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.0000000000                  NaN          1.000000000
## Lag 1e+05  0.0615320003                  NaN          0.008648500
## Lag 2e+05  0.0131933863                  NaN          0.002363327
## Lag 3e+05  0.0145662562                  NaN         -0.021144796
## Lag 4e+05 -0.0001028329                  NaN          0.004605311
## Lag 5e+05 -0.0117710861                  NaN         -0.006542717
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.000000e+00     1.0000000000
## Lag 1e+05         5.828953e-02     0.0413255793
## Lag 2e+05         1.669416e-02     0.0296014703
## Lag 3e+05        -3.045154e-04     0.0151323854
## Lag 4e+05        -8.097977e-04     0.0185442568
## Lag 5e+05         7.793517e-05     0.0002339497
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.077696836           0.046630989           0.058127699
## Lag 2e+05 -0.025724814           0.015663793          -0.004647695
## Lag 3e+05  0.002623648          -0.011594296          -0.003597631
## Lag 4e+05  0.003123766          -0.001879833          -0.009278866
## Lag 5e+05  0.014516481           0.011752878          -0.035606859
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.046352258           0.04437786          0.061165180
## Lag 2e+05           0.023465073          -0.01159443         -0.012776914
## Lag 3e+05           0.009716968          -0.01642283         -0.005951031
## Lag 4e+05          -0.001017426           0.01204753          0.016178097
## Lag 5e+05           0.016503382           0.02560360          0.028320028
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000                  NaN          1.000000000
## Lag 1e+05  0.074654952                  NaN          0.006519612
## Lag 2e+05 -0.020973543                  NaN          0.033600248
## Lag 3e+05  0.009058755                  NaN          0.002376476
## Lag 4e+05  0.005957942                  NaN          0.004837043
## Lag 5e+05  0.012842164                  NaN          0.002099117
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000     1.0000000000
## Lag 1e+05         0.0591780560     0.0395546688
## Lag 2e+05        -0.0182799740    -0.0242549062
## Lag 3e+05        -0.0023655605    -0.0005791896
## Lag 4e+05         0.0005461984     0.0132921883
## Lag 5e+05         0.0118854513     0.0064418277
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.044738477           0.040947178           0.084440683
## Lag 2e+05  0.002884406           0.016620349           0.001189156
## Lag 3e+05  0.019576979           0.012757643          -0.009502296
## Lag 4e+05 -0.006912166          -0.002845044           0.012764862
## Lag 5e+05  0.020142443           0.026003441          -0.018994340
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.047571280          0.038274072          0.019788329
## Lag 2e+05           0.012631999         -0.004459196         -0.011720044
## Lag 3e+05           0.021484150          0.037634586         -0.006519736
## Lag 4e+05           0.005500457          0.009080247          0.005751811
## Lag 5e+05          -0.019368217         -0.018070103          0.034949691
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000                  NaN          1.000000000
## Lag 1e+05  0.043831741                  NaN          0.029995688
## Lag 2e+05  0.009874142                  NaN          0.003121192
## Lag 3e+05  0.015340495                  NaN         -0.015125952
## Lag 4e+05 -0.008939917                  NaN          0.001401034
## Lag 5e+05  0.027568780                  NaN         -0.017390746
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000
## Lag 1e+05          0.026753737       0.03062275
## Lag 2e+05          0.007933508      -0.01228077
## Lag 3e+05          0.005083573       0.01681077
## Lag 4e+05          0.007429634      -0.01686335
## Lag 5e+05          0.024759211       0.01151237
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.039615260          0.0377373270           0.045646159
## Lag 2e+05  0.002749847          0.0146719501           0.014708912
## Lag 3e+05 -0.030845677         -0.0197613622          -0.009769839
## Lag 4e+05 -0.043227288         -0.0336113163           0.008155109
## Lag 5e+05 -0.009303895         -0.0003519172           0.013477730
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.000000e+00          1.000000000
## Lag 1e+05          -0.002300675         4.096343e-02          0.032032596
## Lag 2e+05           0.011573594        -4.587212e-05         -0.003868606
## Lag 3e+05          -0.011789778        -1.213016e-02          0.005509995
## Lag 4e+05          -0.002942026        -2.304804e-02         -0.007092339
## Lag 5e+05          -0.008286256        -1.612553e-02         -0.003085168
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.0000000000                  NaN          1.000000000
## Lag 1e+05  0.0465667959                  NaN          0.008187257
## Lag 2e+05  0.0185101848                  NaN         -0.016341321
## Lag 3e+05 -0.0271680190                  NaN         -0.006584918
## Lag 4e+05 -0.0510129987                  NaN         -0.012571578
## Lag 5e+05 -0.0007495809                  NaN          0.002402709
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000     1.000000e+00
## Lag 1e+05          0.037120885     2.964768e-02
## Lag 2e+05          0.019716109     7.075785e-05
## Lag 3e+05         -0.018726892    -2.113334e-02
## Lag 4e+05         -0.024378057     3.918652e-03
## Lag 5e+05         -0.009646746    -1.568663e-02
## Chain 6 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000            1.00000000
## Lag 1e+05  0.03510402           0.045241680            0.07707384
## Lag 2e+05  0.02222387           0.016612169            0.01998713
## Lag 3e+05 -0.01789309          -0.008707663           -0.02245937
## Lag 4e+05 -0.02997694           0.020969182           -0.04091300
## Lag 5e+05  0.01230665           0.009793536           -0.01032447
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000         1.0000000000
## Lag 1e+05          0.0336637287          0.034956860         0.0036040378
## Lag 2e+05         -0.0335325890          0.006817437         0.0145603565
## Lag 3e+05         -0.0212705984         -0.002997783        -0.0004607675
## Lag 4e+05         -0.0001777812         -0.012700251        -0.0017717964
## Lag 5e+05          0.0047181295         -0.013151350        -0.0007571268
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.00000000                  NaN         1.0000000000
## Lag 1e+05  0.03161926                  NaN         0.0077456003
## Lag 2e+05  0.01616274                  NaN         0.0207564724
## Lag 3e+05 -0.01450608                  NaN         0.0054408026
## Lag 4e+05 -0.02565314                  NaN         0.0001642485
## Lag 5e+05  0.01616452                  NaN        -0.0199226353
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000
## Lag 1e+05          0.010272625      0.005485509
## Lag 2e+05          0.024179288      0.037192089
## Lag 3e+05         -0.027057817     -0.001482983
## Lag 4e+05         -0.011931759     -0.030545251
## Lag 5e+05          0.003060226      0.020255973
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.058893469           0.039965270          0.0970508984
## Lag 2e+05 -0.003261214          -0.005838929         -0.0202830320
## Lag 3e+05 -0.010356870          -0.022393907          0.0009710117
## Lag 4e+05 -0.030328372          -0.018065643          0.0142815565
## Lag 5e+05  0.005329400           0.003885156          0.0268060597
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.025593257          0.028953242          0.037749161
## Lag 2e+05          -0.029328941          0.007382991         -0.003384296
## Lag 3e+05           0.009835422         -0.019876383          0.021210853
## Lag 4e+05           0.014966574         -0.010259903         -0.029336501
## Lag 5e+05           0.011861057          0.005378481         -0.019210676
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000                  NaN          1.000000000
## Lag 1e+05  0.062691470                  NaN          0.035145579
## Lag 2e+05 -0.006684255                  NaN          0.023379840
## Lag 3e+05 -0.010898026                  NaN         -0.001962925
## Lag 4e+05 -0.034322299                  NaN          0.035395273
## Lag 5e+05  0.013456784                  NaN         -0.010391922
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05         0.0485916011      0.028650903
## Lag 2e+05         0.0083355466     -0.004286094
## Lag 3e+05        -0.0045569884     -0.012022407
## Lag 4e+05        -0.0276754105     -0.030301087
## Lag 5e+05        -0.0004347379      0.004342911
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.063519282           0.058167058           0.058256080
## Lag 2e+05  0.024724893           0.032191421          -0.012554003
## Lag 3e+05 -0.024553261          -0.014089360          -0.005774850
## Lag 4e+05  0.005291724           0.009323147           0.025345020
## Lag 5e+05  0.012365026           0.019536362           0.005907464
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000         1.0000000000          1.000000000
## Lag 1e+05          0.0592417955         0.0580704770          0.045737900
## Lag 2e+05          0.0236312583         0.0036273837         -0.002437791
## Lag 3e+05          0.0006360558        -0.0018159307         -0.021784404
## Lag 4e+05          0.0005667996        -0.0022437904         -0.018677511
## Lag 5e+05         -0.0120207749        -0.0003718623          0.027795001
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000                  NaN          1.000000000
## Lag 1e+05  0.071652696                  NaN          0.025356743
## Lag 2e+05  0.028727811                  NaN          0.024539468
## Lag 3e+05 -0.027932774                  NaN          0.005422581
## Lag 4e+05 -0.001143187                  NaN         -0.001770757
## Lag 5e+05  0.015362562                  NaN          0.006658790
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0             1.0000000000      1.000000000
## Lag 1e+05         0.0441475904      0.035370401
## Lag 2e+05         0.0254787777      0.009232802
## Lag 3e+05        -0.0118251284     -0.018209615
## Lag 4e+05         0.0007849232      0.015249365
## Lag 5e+05         0.0138695845      0.004689952
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.31929               0.29645              -0.57133 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.83368               0.05405              -0.18778 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.20974                   NaN               0.49466 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               1.04046               0.56691 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7495035             0.7668873             0.5677749 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.4044636             0.9568969             0.8510464 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.8338745                   NaN             0.6208423 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.2981243             0.5707730 
## Joint P-value (lower = worse):  0.8516579 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               1.13193               0.39303               0.26194 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               1.21982               1.17096               1.57711 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.91553                   NaN               0.09126 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               0.82483               0.90333 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.2576630             0.6942963             0.7933644 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.2225344             0.2416153             0.1147704 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.3599129                   NaN             0.9272884 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.4094671             0.3663501 
## Joint P-value (lower = worse):  0.7071803 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.05769              -0.06097              -0.87645 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.50540               0.26183               0.40963 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.41886                   NaN               0.39725 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               0.85446               0.05290 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.9539982             0.9513828             0.3807877 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.6132747             0.7934488             0.6820808 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.6753189                   NaN             0.6911840 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.3928501             0.9578117 
## Joint P-value (lower = worse):  0.8182979 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              0.159390              0.407568              0.105578 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              0.001864              1.235550             -0.071216 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              0.479604                   NaN              0.170364 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##              0.179313              1.562015 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8733618             0.6835910             0.9159174 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.9985128             0.2166260             0.9432261 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.6315091                   NaN             0.8647241 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.8576920             0.1182843 
## Joint P-value (lower = worse):  0.2241559 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                2.0429                2.3075                1.5855 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.2320                0.9142                1.5249 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                2.0976                   NaN               -0.3089 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                1.6555                1.0748 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.04106374            0.02102804            0.11286353 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.81653614            0.36063357            0.12728410 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.03594265                   NaN            0.75742239 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.09782292            0.28244962 
## Joint P-value (lower = worse):  0.5779967 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.80306               0.28290              -0.50234 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.85255               0.25453              -0.21654 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.56866                   NaN               0.03293 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               0.84810               0.75193 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.4219413             0.7772502             0.6154269 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.3939072             0.7990827             0.8285652 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.5695842                   NaN             0.9737299 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.3963814             0.4520955 
## Joint P-value (lower = worse):  0.8319107 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                2.0072                1.6255                0.7333 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                0.3374                1.2726                1.2668 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                1.8125                   NaN               -1.7645 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##                1.9419                0.7377 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.04472874            0.10404638            0.46336318 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.73580166            0.20314824            0.20521447 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.06991079                   NaN            0.07765521 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##            0.05214668            0.46067462 
## Joint P-value (lower = worse):  0.2015491 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.19740               0.44517              -0.33093 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.59032               1.95262               0.13172 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.26815                   NaN               1.43111 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##               0.82471              -0.06027 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.8435119             0.6561966             0.7406963 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.5549772             0.0508651             0.8952041 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.7885858                   NaN             0.1523987 
##  nodematch.race..wa.O      absdiff.sqrt.age 
##             0.4095336             0.9519420 
## Joint P-value (lower = worse):  0.200977 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 8

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                 -35.7820 59.295  0.34234        0.39346
## nodefactor.deg.main.1  -0.1979 62.302  0.35970        0.40652
## nodefactor.race..wa.B  36.9469 19.156  0.11060        0.12957
## nodefactor.race..wa.H  37.0260 25.121  0.14503        0.17140
## nodefactor.region.EW    0.8276 30.354  0.17525        0.25524
## nodefactor.region.OW    0.5847 60.227  0.34772        0.41981
## concurrent              0.5583 53.118  0.30668        0.35894
## nodematch.race..wa.B   -8.4768  0.000  0.00000        0.00000
## nodematch.race..wa.H  -36.3779  3.902  0.02253        0.02448
## nodematch.race..wa.O   36.1499 45.274  0.26139        0.29955
## nodematch.region        0.5006 51.171  0.29544        0.34839
## absdiff.sqrt.age        0.3384 58.776  0.33935        0.36948
## 
## 2. Quantiles for each variable:
## 
##                            2.5%     25%        50%     75%   97.5%
## edges                 -152.0000 -76.000 -3.600e+01   4.000  82.000
## nodefactor.deg.main.1 -122.0000 -43.000 -1.000e+00  41.000 123.000
## nodefactor.race..wa.B   -0.1648  23.835  3.684e+01  49.835  74.835
## nodefactor.race..wa.H  -11.9080  20.092  3.709e+01  54.092  87.092
## nodefactor.region.EW   -57.6796 -19.680  3.204e-01  21.320  61.320
## nodefactor.region.OW  -117.5480 -39.548  4.520e-01  41.452 119.452
## concurrent            -102.0000 -36.000 -2.274e-13  36.000 106.000
## nodematch.race..wa.B    -8.4768  -8.477 -8.477e+00  -8.477  -8.477
## nodematch.race..wa.H   -43.1997 -39.200 -3.620e+01 -34.200 -28.200
## nodematch.race..wa.O   -51.8442   5.156  3.616e+01  66.156 125.181
## nodematch.region       -99.4000 -34.400  6.000e-01  34.600 102.600
## absdiff.sqrt.age      -112.6073 -39.800  3.449e-02  39.885 117.680
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                           edges nodefactor.deg.main.1
## edges                 1.0000000             0.8198562
## nodefactor.deg.main.1 0.8198562             1.0000000
## nodefactor.race..wa.B 0.4313005             0.3255904
## nodefactor.race..wa.H 0.5072312             0.4342230
## nodefactor.region.EW  0.3748150             0.3037192
## nodefactor.region.OW  0.6174627             0.4524967
## concurrent            0.9571991             0.7821126
## nodematch.race..wa.B         NA                    NA
## nodematch.race..wa.H  0.1108398             0.1008701
## nodematch.race..wa.O  0.8553047             0.7037508
## nodematch.region      0.9382544             0.7732798
## absdiff.sqrt.age      0.8514302             0.7009786
##                       nodefactor.race..wa.B nodefactor.race..wa.H
## edges                           0.431300539            0.50723120
## nodefactor.deg.main.1           0.325590396            0.43422300
## nodefactor.race..wa.B           1.000000000            0.04166726
## nodefactor.race..wa.H           0.041667261            1.00000000
## nodefactor.region.EW            0.086053130            0.32861488
## nodefactor.region.OW            0.240104208            0.29709720
## concurrent                      0.415538244            0.48644419
## nodematch.race..wa.B                     NA                    NA
## nodematch.race..wa.H            0.002631761            0.40541424
## nodematch.race..wa.O            0.118865292            0.12676572
## nodematch.region                0.414601042            0.45997878
## absdiff.sqrt.age                0.370419675            0.42904036
##                       nodefactor.region.EW nodefactor.region.OW concurrent
## edges                           0.37481503           0.61746268  0.9571991
## nodefactor.deg.main.1           0.30371915           0.45249669  0.7821126
## nodefactor.race..wa.B           0.08605313           0.24010421  0.4155382
## nodefactor.race..wa.H           0.32861488           0.29709720  0.4864442
## nodefactor.region.EW            1.00000000           0.10533570  0.3552855
## nodefactor.region.OW            0.10533570           1.00000000  0.5838964
## concurrent                      0.35528551           0.58389644  1.0000000
## nodematch.race..wa.B                    NA                   NA         NA
## nodematch.race..wa.H            0.12067322           0.05969139  0.1050691
## nodematch.race..wa.O            0.28254395           0.54738617  0.8169549
## nodematch.region                0.25642670           0.55057754  0.8997185
## absdiff.sqrt.age                0.31516876           0.52609908  0.8130123
##                       nodematch.race..wa.B nodematch.race..wa.H
## edges                                   NA          0.110839823
## nodefactor.deg.main.1                   NA          0.100870055
## nodefactor.race..wa.B                   NA          0.002631761
## nodefactor.race..wa.H                   NA          0.405414238
## nodefactor.region.EW                    NA          0.120673221
## nodefactor.region.OW                    NA          0.059691394
## concurrent                              NA          0.105069131
## nodematch.race..wa.B                     1                   NA
## nodematch.race..wa.H                    NA          1.000000000
## nodematch.race..wa.O                    NA          0.005288206
## nodematch.region                        NA          0.097667912
## absdiff.sqrt.age                        NA          0.089605887
##                       nodematch.race..wa.O nodematch.region
## edges                          0.855304725       0.93825439
## nodefactor.deg.main.1          0.703750753       0.77327975
## nodefactor.race..wa.B          0.118865292       0.41460104
## nodefactor.race..wa.H          0.126765725       0.45997878
## nodefactor.region.EW           0.282543954       0.25642670
## nodefactor.region.OW           0.547386172       0.55057754
## concurrent                     0.816954865       0.89971846
## nodematch.race..wa.B                    NA               NA
## nodematch.race..wa.H           0.005288206       0.09766791
## nodematch.race..wa.O           1.000000000       0.80658655
## nodematch.region               0.806586547       1.00000000
## absdiff.sqrt.age               0.728039717       0.79747877
##                       absdiff.sqrt.age
## edges                       0.85143015
## nodefactor.deg.main.1       0.70097862
## nodefactor.race..wa.B       0.37041968
## nodefactor.race..wa.H       0.42904036
## nodefactor.region.EW        0.31516876
## nodefactor.region.OW        0.52609908
## concurrent                  0.81301231
## nodematch.race..wa.B                NA
## nodematch.race..wa.H        0.08960589
## nodematch.race..wa.O        0.72803972
## nodematch.region            0.79747877
## absdiff.sqrt.age            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05  0.13971319           0.128702939           0.173156261
## Lag 2e+05  0.04388442           0.017428892           0.026353539
## Lag 3e+05  0.02793700           0.023611888          -0.010118768
## Lag 4e+05  0.01163875           0.009140314           0.007706176
## Lag 5e+05 -0.03605219          -0.016748793           0.036056880
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05           0.145440311           0.28833616          0.141208526
## Lag 2e+05           0.035544991           0.12817619          0.020048547
## Lag 3e+05           0.024434841           0.07481302         -0.009825489
## Lag 4e+05           0.036250854           0.03621096          0.001683992
## Lag 5e+05          -0.003411868          -0.02886311          0.016044216
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.00000000                  NaN          1.000000000
## Lag 1e+05  0.14899222                  NaN          0.058539472
## Lag 2e+05  0.04456520                  NaN          0.017563689
## Lag 3e+05  0.04261077                  NaN          0.007434991
## Lag 4e+05  0.01129189                  NaN          0.012493246
## Lag 5e+05 -0.03392751                  NaN         -0.011574612
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000      1.000000000       1.00000000
## Lag 1e+05           0.10849781      0.179369327       0.07051678
## Lag 2e+05           0.02941705      0.051163363       0.02766081
## Lag 3e+05           0.03041099      0.023195251       0.01841126
## Lag 4e+05           0.01295223     -0.004545038       0.01451789
## Lag 5e+05          -0.02834869     -0.032306862      -0.03173324
## Chain 2 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000            1.00000000
## Lag 1e+05  0.139257733          0.1236898540            0.17703540
## Lag 2e+05  0.039885287          0.0222384890            0.04038188
## Lag 3e+05  0.007649351         -0.0003423886            0.01880108
## Lag 4e+05  0.020154399          0.0205154954            0.01354788
## Lag 5e+05 -0.014605755         -0.0118724504            0.02344193
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000           1.00000000
## Lag 1e+05           0.135081059           0.31651585           0.18993733
## Lag 2e+05           0.049026835           0.17148222           0.03081549
## Lag 3e+05           0.009777504           0.07647565           0.04574935
## Lag 4e+05           0.008157433           0.04631051           0.01882128
## Lag 5e+05           0.007205317           0.02059672           0.02226556
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000                  NaN          1.000000000
## Lag 1e+05  0.159454998                  NaN          0.081580325
## Lag 2e+05  0.035118539                  NaN          0.018549964
## Lag 3e+05  0.003395846                  NaN          0.004348450
## Lag 4e+05  0.021246327                  NaN          0.011242735
## Lag 5e+05 -0.001371869                  NaN         -0.002875158
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.103927100      0.163083386      0.083842318
## Lag 2e+05          0.030810679      0.033412178      0.001700140
## Lag 3e+05         -0.011384791      0.012962981     -0.006222393
## Lag 4e+05          0.021420422      0.021858736      0.013806289
## Lag 5e+05         -0.001584552     -0.009737552     -0.002305830
## Chain 3 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.137242333           0.101411896           0.150913838
## Lag 2e+05  0.034127482           0.021568915           0.023130534
## Lag 3e+05 -0.004748598           0.008249099           0.003315418
## Lag 4e+05  0.001972682           0.011383057           0.028570061
## Lag 5e+05 -0.002808179           0.025915161           0.015094673
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000         1.0000000000
## Lag 1e+05           0.133977644           0.28919325         0.1900624184
## Lag 2e+05           0.043647415           0.11264335         0.0580636929
## Lag 3e+05           0.018218862           0.03971111         0.0052205931
## Lag 4e+05           0.006332031           0.02192362         0.0008449884
## Lag 5e+05           0.003402631           0.03567313        -0.0191547430
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.0000000000                  NaN          1.000000000
## Lag 1e+05  0.1421752122                  NaN          0.052919078
## Lag 2e+05  0.0397052317                  NaN          0.019553023
## Lag 3e+05  0.0015623460                  NaN          0.014993593
## Lag 4e+05 -0.0004566350                  NaN         -0.003469483
## Lag 5e+05 -0.0004799436                  NaN          0.003324468
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.122637676      0.154499760      0.087826922
## Lag 2e+05          0.019444208      0.059356768      0.015698532
## Lag 3e+05         -0.024867966     -0.001640872     -0.012435788
## Lag 4e+05         -0.008797848      0.012504498      0.001929872
## Lag 5e+05         -0.013465427      0.002004555     -0.002365604
## Chain 4 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000          1.0000000000
## Lag 1e+05  0.132411198           0.118404865          0.1466966914
## Lag 2e+05  0.041240636           0.033827086          0.0220972789
## Lag 3e+05  0.027181249          -0.002651682          0.0197065228
## Lag 4e+05  0.003159960           0.007963159          0.0048696860
## Lag 5e+05 -0.004083949          -0.004528368         -0.0003965168
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.136478748          0.295991729          0.168672279
## Lag 2e+05           0.046244229          0.156852345          0.053627847
## Lag 3e+05           0.008808341          0.093329794          0.036151661
## Lag 4e+05          -0.002983562          0.044928675          0.013543004
## Lag 5e+05          -0.025175633          0.007077879         -0.004476381
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.0000000000                  NaN           1.00000000
## Lag 1e+05 0.1405826312                  NaN           0.07332129
## Lag 2e+05 0.0548814320                  NaN           0.01561381
## Lag 3e+05 0.0278564204                  NaN           0.01305728
## Lag 4e+05 0.0026985105                  NaN          -0.01125248
## Lag 5e+05 0.0008531475                  NaN           0.00464648
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.108127072      0.154246430      0.047006389
## Lag 2e+05          0.016757293      0.037275748      0.022962028
## Lag 3e+05          0.002643946      0.034484208      0.021749032
## Lag 4e+05          0.011558008     -0.004668933     -0.001762831
## Lag 5e+05          0.012306685     -0.003486687     -0.011952077
## Chain 5 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000            1.00000000           1.000000000
## Lag 1e+05  0.141632717            0.13988375           0.151326165
## Lag 2e+05  0.044780577            0.04856182           0.031116774
## Lag 3e+05  0.002162591            0.01085628           0.002043548
## Lag 4e+05 -0.031498903           -0.02181973          -0.009609612
## Lag 5e+05 -0.015614432           -0.01026008           0.001839964
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000            1.0000000          1.000000000
## Lag 1e+05           0.165368313            0.2934309          0.176168153
## Lag 2e+05           0.040886259            0.1330356          0.050174488
## Lag 3e+05           0.038925506            0.0699241          0.001356149
## Lag 4e+05           0.007221700            0.0392311          0.001833477
## Lag 5e+05           0.007605722            0.0333197         -0.003294361
##              concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.0000000000                  NaN           1.00000000
## Lag 1e+05  0.1400503276                  NaN           0.13640114
## Lag 2e+05  0.0415892957                  NaN           0.01569790
## Lag 3e+05 -0.0006796036                  NaN           0.01853395
## Lag 4e+05 -0.0237960053                  NaN          -0.01815252
## Lag 5e+05 -0.0070499191                  NaN           0.00053267
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000     1.0000000000      1.000000000
## Lag 1e+05          0.123450227     0.1585001120      0.085275553
## Lag 2e+05          0.044752796     0.0453479661      0.049727374
## Lag 3e+05         -0.006612228     0.0002072572      0.007652525
## Lag 4e+05         -0.006875040    -0.0364444511     -0.031127014
## Lag 5e+05         -0.024700729    -0.0199476534     -0.021621214
## Chain 6 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.126652709          0.1006060545           0.141426324
## Lag 2e+05  0.038973360          0.0193448022           0.033730916
## Lag 3e+05  0.018053761          0.0168158799           0.002246293
## Lag 4e+05 -0.004183880          0.0002421591          -0.013890067
## Lag 5e+05  0.002674925         -0.0150210666          -0.001336727
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000         1.0000000000
## Lag 1e+05           0.157705846           0.28880558         0.1634654174
## Lag 2e+05           0.056342907           0.11511483         0.0352999113
## Lag 3e+05           0.015501619           0.02893429         0.0199178616
## Lag 4e+05           0.001663469           0.02022821        -0.0009923958
## Lag 5e+05          -0.011673451           0.02558545        -0.0071625349
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000                  NaN           1.00000000
## Lag 1e+05  0.133595614                  NaN           0.10040223
## Lag 2e+05  0.048797526                  NaN           0.03310809
## Lag 3e+05  0.021810886                  NaN           0.01646567
## Lag 4e+05 -0.004953879                  NaN           0.01673215
## Lag 5e+05  0.005057670                  NaN           0.01337412
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000     1.0000000000      1.000000000
## Lag 1e+05          0.108791112     0.1465602605      0.083488144
## Lag 2e+05          0.021704060     0.0289032449      0.007060473
## Lag 3e+05          0.010127895     0.0307439544      0.009086866
## Lag 4e+05          0.002295527     0.0025189541      0.001064291
## Lag 5e+05         -0.009318144     0.0009325025      0.009446983
## Chain 7 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0     1.0000000000           1.000000000           1.000000000
## Lag 1e+05 0.1264582298           0.132872793           0.162770710
## Lag 2e+05 0.0372252399           0.027444154           0.034538376
## Lag 3e+05 0.0154910213           0.006424169           0.006592691
## Lag 4e+05 0.0008694627           0.021304173          -0.004365250
## Lag 5e+05 0.0015253506           0.016235147          -0.009295880
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0                1.00000000           1.00000000          1.000000000
## Lag 1e+05            0.11602679           0.30520589          0.160730916
## Lag 2e+05            0.05014190           0.15592693          0.058814911
## Lag 3e+05            0.01188580           0.10153333          0.034323294
## Lag 4e+05            0.01061362           0.05213105          0.004769035
## Lag 5e+05            0.01520907           0.04346484          0.001555296
##            concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0     1.000000000                  NaN          1.000000000
## Lag 1e+05 0.134512045                  NaN          0.084763828
## Lag 2e+05 0.052206934                  NaN          0.021466603
## Lag 3e+05 0.005622392                  NaN          0.011887398
## Lag 4e+05 0.002353528                  NaN         -0.007245768
## Lag 5e+05 0.000712005                  NaN          0.012951911
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.099314741      0.163502064      0.075288236
## Lag 2e+05          0.024496040      0.046804710      0.023592385
## Lag 3e+05          0.030811454      0.013832674     -0.005685714
## Lag 4e+05          0.004813101     -0.007391885      0.004863145
## Lag 5e+05         -0.010770395      0.002017110     -0.001830256
## Chain 8 
##                  edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.124596408           0.118507743           0.150316393
## Lag 2e+05  0.017544792           0.033917486           0.026181270
## Lag 3e+05  0.002460649          -0.005494438          -0.006594472
## Lag 4e+05  0.002107418           0.027831903          -0.007480336
## Lag 5e+05 -0.006216059           0.013157232          -0.020377980
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000           1.00000000
## Lag 1e+05           0.131680960           0.28229332           0.17385062
## Lag 2e+05           0.027421050           0.11593237           0.03422530
## Lag 3e+05          -0.006478922           0.06810939           0.01320214
## Lag 4e+05           0.015781997           0.05505186          -0.02643374
## Lag 5e+05           0.019195731           0.04873151          -0.01407026
##             concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0      1.000000000                  NaN           1.00000000
## Lag 1e+05  0.128069589                  NaN           0.06632974
## Lag 2e+05  0.012360827                  NaN           0.02961276
## Lag 3e+05 -0.003899899                  NaN           0.01370616
## Lag 4e+05 -0.002218306                  NaN           0.03467683
## Lag 5e+05  0.006621515                  NaN           0.02249790
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000      1.000000000      1.000000000
## Lag 1e+05          0.108513845      0.137826049      0.070734338
## Lag 2e+05          0.031524743      0.028119900      0.007289788
## Lag 3e+05          0.017275442      0.002497145      0.020008536
## Lag 4e+05         -0.002826534      0.004747343     -0.006803347
## Lag 5e+05         -0.006204573     -0.004911061      0.001279983
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -1.50199              -0.33035               0.05455 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##              -0.11540              -1.29528              -1.00443 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -1.38681                   NaN              -0.38175 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##              -2.14429              -1.48630              -1.51695 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.13309930            0.74113478            0.95649587 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.90812452            0.19522341            0.31517117 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.16549919                   NaN            0.70264816 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.03200964            0.13720074            0.12927893 
## Joint P-value (lower = worse):  0.7325997 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             -0.373597             -0.002778              0.153684 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             -0.910452             -0.078500             -0.316979 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             -0.392432                   NaN             -0.905711 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             -0.147853             -0.475863             -0.295566 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.7087043             0.9977836             0.8778586 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.3625844             0.9374307             0.7512598 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.6947388                   NaN             0.3650889 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.8824587             0.6341717             0.7675614 
## Joint P-value (lower = worse):  0.9993302 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.26213              -0.41795               1.92096 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.42985              -0.03358               0.18094 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.42156                   NaN               0.16835 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##              -0.83883               0.45495               0.23003 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.79321890            0.67598452            0.05473736 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.66730787            0.97321105            0.85641783 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.67334888                   NaN            0.86630645 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.40156267            0.64914415            0.81806899 
## Joint P-value (lower = worse):  0.8245553 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.3894               -0.3684                0.5667 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.2165               -0.6071                0.8934 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.3927                   NaN                0.0000 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               -0.6312               -0.1372               -0.4512 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6970068             0.7125753             0.5708852 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8285873             0.5437846             0.3716294 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.6945343                   NaN             1.0000000 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.5278935             0.8909076             0.6518198 
## Joint P-value (lower = worse):  0.9857331 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               -0.2975                0.2447                0.8309 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##                1.0881               -0.2660               -0.3648 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               -0.3161                   NaN                2.3944 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##               -1.2112               -0.5702                0.1637 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##            0.76605487            0.80665071            0.40603527 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##            0.27657206            0.79020291            0.71524540 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##            0.75194186                   NaN            0.01664686 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##            0.22581616            0.56852261            0.86994743 
## Joint P-value (lower = worse):  0.5653305 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##                0.4502                0.4341               -0.1836 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               -0.2607               -1.5523               -0.1754 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##                0.0174                   NaN               -0.1753 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##                0.8269                0.1675                0.5806 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6525496             0.6642188             0.8543387 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.7943405             0.1205855             0.8607860 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9861204                   NaN             0.8608347 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.4082851             0.8669399             0.5615391 
## Joint P-value (lower = worse):  0.509544 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##               0.04301               0.37883               0.09940 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               0.18548               0.85814              -0.29555 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##               0.33064                   NaN              -0.59660 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##              -0.12550              -0.17051               0.71257 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.9656904             0.7048111             0.9208173 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.8528523             0.3908162             0.7675702 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.7409175                   NaN             0.5507773 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.9001254             0.8646119             0.4761119 
## Joint P-value (lower = worse):  0.8187205 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##              -0.44283              -0.44618              -0.90896 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##               1.49621              -0.92802              -0.08809 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##              -0.07957                   NaN               0.77312 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##              -1.13582              -0.07573              -0.19772 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.deg.main.1 nodefactor.race..wa.B 
##             0.6578883             0.6554702             0.3633725 
## nodefactor.race..wa.H  nodefactor.region.EW  nodefactor.region.OW 
##             0.1345989             0.3533960             0.9298082 
##            concurrent  nodematch.race..wa.B  nodematch.race..wa.H 
##             0.9365787                   NaN             0.4394507 
##  nodematch.race..wa.O      nodematch.region      absdiff.sqrt.age 
##             0.2560311             0.9396312             0.8432625 
## Joint P-value (lower = worse):  0.6203305 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Summary of model fit

Model 1

summary(est.p.buildup.unbal[[1]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x56421a88a210>
## 
## Iterations:  81 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                   -9.9199     0.0249      0  <1e-04 ***
## deg3+                      -Inf     0.0000      0  <1e-04 ***
## nodematch.role.class.I     -Inf     0.0000      0  <1e-04 ***
## nodematch.role.class.R     -Inf     0.0000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 2

summary(est.p.buildup.unbal[[2]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x56423e8a3d08>
## 
## Iterations:  85 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -9.89378    0.02871      0  <1e-04 ***
## nodefactor.race..wa.B   0.01789    0.06920      0  0.7961    
## nodefactor.race..wa.H  -0.13659    0.05609      0  0.0149 *  
## deg3+                      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.I     -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R     -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 3

summary(est.p.buildup.unbal[[3]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x56425c9e9550>
## 
## Iterations:  115 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                     607.5         NA     NA      NA    
## nodefactor.race..wa.B    -617.1         NA     NA      NA    
## nodefactor.race..wa.H    -617.4         NA     NA      NA    
## nodematch.race..wa.B      601.4         NA     NA      NA    
## nodematch.race..wa.H      616.8         NA     NA      NA    
## nodematch.race..wa.O     -617.6         NA     NA      NA    
## deg3+                      -Inf        0.0      0  <1e-04 ***
## nodematch.role.class.I     -Inf        0.0      0  <1e-04 ***
## nodematch.role.class.R     -Inf        0.0      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 4

summary(est.p.buildup.unbal[[4]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x56427ad87818>
## 
## Iterations:  122 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                   615.74466         NA     NA      NA    
## nodefactor.deg.main.1    -0.09889    0.03399      0 0.00362 ** 
## nodefactor.race..wa.B  -625.22211         NA     NA      NA    
## nodefactor.race..wa.H  -625.48897         NA     NA      NA    
## nodematch.race..wa.B    546.72404         NA     NA      NA    
## nodematch.race..wa.H    624.93912         NA     NA      NA    
## nodematch.race..wa.O   -625.66862         NA     NA      NA    
## deg3+                        -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R       -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 5

summary(est.p.buildup.unbal[[5]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x56429923a080>
## 
## Iterations:  111 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC %  p-value    
## edges                   33.31402         NA     NA       NA    
## nodefactor.deg.main.1   -0.11335    0.03413      0 0.000896 ***
## nodefactor.race..wa.B  -42.65208         NA     NA       NA    
## nodefactor.race..wa.H  -42.88768         NA     NA       NA    
## nodefactor.region.EW    -0.16414    0.05959      0 0.005882 ** 
## nodefactor.region.OW    -0.18322    0.03773      0  < 1e-04 ***
## nodematch.race..wa.B    18.68528         NA     NA       NA    
## nodematch.race..wa.H    42.34976         NA     NA       NA    
## nodematch.race..wa.O   -43.07694         NA     NA       NA    
## deg3+                       -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 6

summary(est.p.buildup.unbal[[6]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5642b7805ef0>
## 
## Iterations:  98 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                   272.08378         NA     NA      NA    
## nodefactor.deg.main.1    -0.11448    0.03404      0 0.00077 ***
## nodefactor.race..wa.B  -280.88536         NA     NA      NA    
## nodefactor.race..wa.H  -281.12788         NA     NA      NA    
## nodefactor.region.EW     -0.17018    0.05950      0 0.00424 ** 
## nodefactor.region.OW     -0.18333    0.03767      0 < 1e-04 ***
## nodematch.race..wa.B    254.02303         NA     NA      NA    
## nodematch.race..wa.H    280.58884         NA     NA      NA    
## nodematch.race..wa.O   -281.31877         NA     NA      NA    
## absdiff.sqrt.age         -0.53450    0.03257      0 < 1e-04 ***
## deg3+                        -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R       -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 7

summary(est.p.buildup.unbal[[7]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + concurrent + 
##     nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") + 
##     degrange(from = 3) + offset(nodematch("role.class", diff = TRUE, 
##     keep = 1:2))
## <environment: 0x5642d5e5d5c8>
## 
## Iterations:  113 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                   50.52480         NA     NA      NA    
## nodefactor.deg.main.1   -0.07950    0.02859      0 0.00543 ** 
## nodefactor.race..wa.B  -61.38140         NA     NA      NA    
## nodefactor.race..wa.H  -61.57400         NA     NA      NA    
## nodefactor.region.EW    -0.12069    0.04938      0 0.01452 *  
## nodefactor.region.OW    -0.12810    0.03142      0 < 1e-04 ***
## concurrent               2.64475    0.06519      0 < 1e-04 ***
## nodematch.race..wa.B    48.53223         NA     NA      NA    
## nodematch.race..wa.H    61.03331         NA     NA      NA    
## nodematch.race..wa.O   -61.75847         NA     NA      NA    
## absdiff.sqrt.age        -0.50948    0.03208      0 < 1e-04 ***
## deg3+                       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Model 8

summary(est.p.buildup.unbal[[8]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + concurrent + 
##     nodematch("race..wa", diff = TRUE) + nodematch("region", 
##     diff = FALSE) + absdiff("sqrt.age") + degrange(from = 3) + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5642f4536290>
## 
## Iterations:  82 out of 400 
## 
## Monte Carlo MLE Results:
##                          Estimate Std. Error MCMC % p-value    
## edges                   178.03815         NA     NA      NA    
## nodefactor.deg.main.1    -0.07969    0.02832      0 0.00489 ** 
## nodefactor.race..wa.B  -190.44423         NA     NA      NA    
## nodefactor.race..wa.H  -190.63071         NA     NA      NA    
## nodefactor.region.EW      0.59640    0.03873      0 < 1e-04 ***
## nodefactor.region.OW      0.16682    0.02195      0 < 1e-04 ***
## concurrent                2.64387    0.06512      0 < 1e-04 ***
## nodematch.race..wa.B    173.35754         NA     NA      NA    
## nodematch.race..wa.H    190.02620         NA     NA      NA    
## nodematch.race..wa.O   -190.82038         NA     NA      NA    
## nodematch.region          1.91123    0.06001      0 < 1e-04 ***
## absdiff.sqrt.age         -0.50961    0.03245      0 < 1e-04 ***
## deg3+                        -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.I       -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R       -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  Warning: The following terms have infinite coefficient estimates:
##   deg3+ 
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419

Network diagnostics

Model 1

(dx_pers1 <- netdx(est.p.buildup.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                        Target Sim Mean Pct Diff Sim SD
## edges                    2018 2057.895     0.02 42.088
## nodefactor.deg.main.1      NA 1845.488       NA 47.615
## nodefactor.race..wa.B      NA  247.272       NA 15.442
## nodefactor.race..wa.H      NA  443.371       NA 21.282
## nodefactor.region.EW       NA  415.548       NA 19.340
## nodefactor.region.OW       NA 1346.859       NA 40.156
## concurrent                 NA  626.428       NA 28.475
## nodematch.race..wa.B       NA    7.352       NA  2.686
## nodematch.race..wa.H       NA   23.814       NA  4.766
## nodematch.race..wa.O       NA 1424.703       NA 35.908
## nodematch.region           NA  908.639       NA 32.078
## absdiff.sqrt.age           NA 2345.125       NA 55.975
## deg3+                      NA    0.000       NA  0.000
## nodematch.role.class.I     NA    0.000       NA  0.000
## nodematch.role.class.R     NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.571   -0.032 30.073
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers1, type="formation")

plot(dx_pers1, type="duration")

plot(dx_pers1, type="dissolution")

Model 2

(dx_pers2 <- netdx(est.p.buildup.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2018.000 2054.666    0.018 41.525
## nodefactor.deg.main.1        NA 1839.277       NA 48.510
## nodefactor.race..wa.B   251.165  257.159    0.024 15.822
## nodefactor.race..wa.H   388.908  395.721    0.018 20.233
## nodefactor.region.EW         NA  412.530       NA 20.538
## nodefactor.region.OW         NA 1351.482       NA 40.068
## concurrent                   NA  626.768       NA 27.840
## nodematch.race..wa.B         NA    8.222       NA  2.871
## nodematch.race..wa.H         NA   19.189       NA  4.279
## nodematch.race..wa.O         NA 1453.966       NA 35.756
## nodematch.region             NA  913.415       NA 30.303
## absdiff.sqrt.age             NA 2343.328       NA 56.223
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.544   -0.033 30.016
## Pct Edges Diss  0.032    0.032    0.002  0.004
plot(dx_pers2, type="formation")

plot(dx_pers2, type="duration")

plot(dx_pers2, type="dissolution")

Model 3

(dx_pers3 <- netdx(est.p.buildup.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2018.000 2018.726    0.000 39.456
## nodefactor.deg.main.1        NA 1800.528       NA 47.466
## nodefactor.race..wa.B   251.165  293.109    0.167 15.135
## nodefactor.race..wa.H   388.908  434.039    0.116 19.772
## nodefactor.region.EW         NA  404.549       NA 19.558
## nodefactor.region.OW         NA 1321.244       NA 40.595
## concurrent                   NA  606.615       NA 28.988
## nodematch.race..wa.B      8.477    0.000   -1.000  0.000
## nodematch.race..wa.H     51.200   15.300   -0.701  3.950
## nodematch.race..wa.O   1246.844 1306.879    0.048 33.908
## nodematch.region             NA  898.638       NA 29.164
## absdiff.sqrt.age             NA 2296.794       NA 56.807
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.538   -0.033 29.972
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers3, type="formation")

plot(dx_pers3, type="duration")

plot(dx_pers3, type="dissolution")

Model 4

(dx_pers4 <- netdx(est.p.buildup.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2018.000 2021.060    0.002 42.433
## nodefactor.deg.main.1  1684.000 1722.241    0.023 48.094
## nodefactor.race..wa.B   251.165  292.842    0.166 14.754
## nodefactor.race..wa.H   388.908  432.905    0.113 20.554
## nodefactor.region.EW         NA  405.103       NA 19.242
## nodefactor.region.OW         NA 1328.640       NA 38.623
## concurrent                   NA  606.778       NA 29.112
## nodematch.race..wa.B      8.477    0.000   -1.000  0.000
## nodematch.race..wa.H     51.200   14.767   -0.712  3.977
## nodematch.race..wa.O   1246.844 1310.079    0.051 35.492
## nodematch.region             NA  896.225       NA 28.455
## absdiff.sqrt.age             NA 2303.437       NA 62.094
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.555   -0.032 30.047
## Pct Edges Diss  0.032    0.032    0.001  0.004
plot(dx_pers4, type="formation")

plot(dx_pers4, type="duration")

plot(dx_pers4, type="dissolution")

Model 5

(dx_pers5 <- netdx(est.p.buildup.unbal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2018.000 2021.875    0.002 41.425
## nodefactor.deg.main.1  1684.000 1721.054    0.022 44.213
## nodefactor.race..wa.B   251.165  292.444    0.164 16.030
## nodefactor.race..wa.H   388.908  435.053    0.119 19.648
## nodefactor.region.EW    367.680  375.074    0.020 19.006
## nodefactor.region.OW   1182.548 1207.386    0.021 36.643
## concurrent                   NA  612.487       NA 28.726
## nodematch.race..wa.B      8.477    0.000   -1.000  0.000
## nodematch.race..wa.H     51.200   15.552   -0.696  3.849
## nodematch.race..wa.O   1246.844 1309.929    0.051 33.890
## nodematch.region             NA  947.001       NA 29.319
## absdiff.sqrt.age             NA 2307.299       NA 60.492
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.588   -0.031 30.016
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers5, type="formation")

plot(dx_pers5, type="duration")

plot(dx_pers5, type="dissolution")

Model 6

(dx_pers6 <- netdx(est.p.buildup.unbal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2018.000 2021.521    0.002 43.189
## nodefactor.deg.main.1  1684.000 1716.523    0.019 45.235
## nodefactor.race..wa.B   251.165  293.533    0.169 16.744
## nodefactor.race..wa.H   388.908  432.091    0.111 19.612
## nodefactor.region.EW    367.680  375.661    0.022 20.040
## nodefactor.region.OW   1182.548 1208.962    0.022 37.301
## concurrent                   NA  613.623       NA 28.184
## nodematch.race..wa.B      8.477    0.000   -1.000  0.000
## nodematch.race..wa.H     51.200   15.251   -0.702  3.680
## nodematch.race..wa.O   1246.844 1311.148    0.052 33.684
## nodematch.region             NA  943.743       NA 30.265
## absdiff.sqrt.age       1665.254 1700.086    0.021 48.025
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.594   -0.031 30.099
## Pct Edges Diss  0.032    0.032   -0.001  0.004
plot(dx_pers6, type="formation")

plot(dx_pers6, type="duration")

plot(dx_pers6, type="dissolution")

Model 7

(dx_pers7 <- netdx(est.p.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.unbal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2018.000 2124.177    0.053 67.419
## nodefactor.deg.main.1  1684.000 1804.936    0.072 66.864
## nodefactor.race..wa.B   251.165  307.149    0.223 19.593
## nodefactor.race..wa.H   388.908  455.242    0.171 25.994
## nodefactor.region.EW    367.680  394.691    0.073 25.683
## nodefactor.region.OW   1182.548 1272.340    0.076 49.874
## concurrent             1385.000 1473.359    0.064 59.084
## nodematch.race..wa.B      8.477    0.000   -1.000  0.000
## nodematch.race..wa.H     51.200   16.309   -0.681  4.242
## nodematch.race..wa.O   1246.844 1378.095    0.105 49.507
## nodematch.region             NA  994.251       NA 37.912
## absdiff.sqrt.age       1665.254 1784.561    0.072 66.569
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.530   -0.033 29.926
## Pct Edges Diss  0.032    0.032    0.002  0.004
plot(dx_pers7, type="formation")

plot(dx_pers7, type="duration")

plot(dx_pers7, type="dissolution")

Model 8

(dx_pers8 <- netdx(est.p.buildup.unbal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                          Target Sim Mean Pct Diff Sim SD
## edges                  2018.000 2123.648    0.052 73.796
## nodefactor.deg.main.1  1684.000 1812.342    0.076 69.314
## nodefactor.race..wa.B   251.165  306.902    0.222 19.369
## nodefactor.race..wa.H   388.908  455.392    0.171 27.489
## nodefactor.region.EW    367.680  395.316    0.075 31.176
## nodefactor.region.OW   1182.548 1275.678    0.079 66.117
## concurrent             1385.000 1472.639    0.063 62.794
## nodematch.race..wa.B      8.477    0.000   -1.000  0.000
## nodematch.race..wa.H     51.200   15.760   -0.692  4.078
## nodematch.race..wa.O   1246.844 1377.114    0.104 55.860
## nodematch.region       1614.400 1728.863    0.071 61.538
## absdiff.sqrt.age       1665.254 1787.982    0.074 69.944
## deg3+                        NA    0.000       NA  0.000
## nodematch.role.class.I       NA    0.000       NA  0.000
## nodematch.role.class.R       NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration  31.571   30.535   -0.033 29.982
## Pct Edges Diss  0.032    0.032    0.000  0.004
plot(dx_pers8, type="formation")

plot(dx_pers8, type="duration")

plot(dx_pers8, type="dissolution")